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In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

Lahore Garrison UniversityLahore, Pakisan.

www.research.lgu.edu.pk

LGURJCSIT

ISSN: 2519-7991(Print)2521-0122 (Online)

Vol. 2 Issue:1January March 2018

Lahore Garrison UniversityResearch Journal of Computer Scienceand Information Technology

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

LGURJCSIT ISSN: 2519-7991(Print), 2521-0122 (Online)

SCOPE OF THE JOURNAL

The LGURJCSIT is an innovative forum for researchers, scientists and engineers in all the domains of computer science and technology to publish high quality, refereed papers. The journal offers articles, survey and review from experts in the field, enhancing insight and understanding of the current trends and state of the art modern technology.

Coverage of the journal includes algorithm and computational complexity, distributed and grid computing, computer architecture and high performance, data communication and networks, pattern recognition and image processing, artificial intelligence, cloud computing, VHDL along with emerging domains like quantum computing, IoT, data sciences, cognitive sciences and Vehicular Automation. Subjective regime is not limited to the aforementioned areas and the Journal policy is to welcome emerging research trends in the general domain of computer science and technology.

SUBMISSION OF ARTICLES

We invite articles with high quality research for publication in all areas of engineering, science and technology. All the manuscripts submitted for publication are firstly peer reviewed to make sure the originality relevancy and readablity. Manuscripts should be submitted via email only.

For submission, emil option using attaced file is strongly encouraged, provided that the text, tables, and figures are included in a single Microsoft Word/Pdf file. Submission guidelines along with official format is available on the following link; www.lgurjcsit.lgu.edu.pk

Contact: For all inquiries, regarding call for papers, submission of research articles and correspondence, kindly contact at this address: LGURJCSIT, Sector C, DHA Phase-VI, Lahore, PakistanPhone: +92- 042-37181823 Email: [email protected]

Copyright @ 2018, Lahore Garrison University, Lahore, Pakistan. All rights reserved.Published by: Department of Computer Science, Lahore Garrison University

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

LGURJCSIT

Volume 2 Issue 1 January - March

Haroon Ur Rashid, Fatma Hussain and Khalid Masood

Big Data and Precision Medicine 01-08

Shamaila Shafiq

Quantum Limits, Computational Complexity and Philosophy – A Review 09-20

Noor Afshan and Kashaf-ud-Duja

Analysis and Evaluation of Requirement Engineering Process at North Bay Solutions 21-30

Muhammad Zulkifl Hasan, Zaka Ullah, Taimoor Hassan and Noor ul Qamar

Cloud Information Security Challenges and Accountable Solutions 31-36

M. Taseer Suleman, M. Hassaan Rafiq, Rafaqat Alam and M. Arsalan Tariq

Reputation based Trust Management System for Improving Public Health Care System in Pakistan 37-44

Minahil Ahmad and Zakka-ur-Rehman

Multi-core commercial off-the-shelf (cots) under the implementation of fault tolerance 45-54

Hafsa Shabbir, Hajira Bibi, Sania Aamir, Muhammad Shan, Mrs Shazia Saqib and Ms. Noor Afshan

Image Segmentation using Marker-Controlled Watershed Transformation and Morphology 55-63

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

LGURJCSIT

Patron in Chief: Major General (R) Obaid Bin ZakriaLahore Garrison University

ADVISORY BOARDMajor General (R) Obaid Bin Zakria, Lahore Garrison University (LGU)Dr. Aasia Khanam, Forman Christian College Lahore, PakistanDr. Asad Raza Kazmi, GCU Lahore,Dr. Wajahat M. Qazi, COMSATS, LahoreDr. Rehan Akbar, University Tunku Abdul Rahman (UTAR) Malaysia Dr. Sagheer Abbas, NCBA&E, LahoreDr. Haider Abbas NUST, IslamabadDr. Atifa Athar, COMSATS, LahoreDr. Asif Shahzad, UET, LahoreDr. Haroon Ur Rashid LGU, LahoreDr. Khalid Masood LGU, LahoreCol. Sohail Ajmal, Director QEC, LGUMr. Tahir Alyas HOD (CS), LGU

EDITORAL BOARDDr. Khalid Masood Lahore Garrison University Lahore, PakistanMr. Nadeem Ali Lahore Garrison University Lahore, PakistanMr. Waqar Azeem Lahore Garrison University Lahore, PakistanMs. Sadia Kausar Lahore Garrison University Lahore, PakistanMs. Binish Zahra Lahore Garrison University Lahore, Pakistan

Chief EditorMrs. Shazia Saqib Lahore Garrison University Lahore, Pakistan

Assistant EditorMr. Umer Farooq Lahore Garrison University Lahore, Pakistan

Correspondence to a manuscript should be send to the chief editor on [email protected], [email protected]

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

LGURJCSIT

REVIEWERS COMMITTEEDr. Yousaf Saeed University of Haripur, Haripur, Pakistan.Dr. Sultan Ullah University of Haripur, Haripur, Pakistan.Dr. Shahzad Asif University of Engineering and Technology, Lahore, Pakistan.Dr. Atifa Athar COMSATS Institute of Information Technology, Lahore, Pakistan.Dr. Asim Qurtuba University of Science and Technology, Peshawar, Pakistan.Dr. Sharifullah Khan School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST).Dr. Kashif Zafar National University of Computer & Emerging Sciences Lahore, Pakistan.Dr. M. Aamer Saleem Ch. Hamdard University Islamabad, Pakistan.Dr. Tahir Naseem International Islamic University Islamabad, Pakistan.Dr. Umer Javeed International Islamic University Islamabad, Pakistan.Dr. Sajjad Ahmed Ghori International Islamic University Islamabad, Pakistan.Dr. A.N. Malik International Islamic University Islamabad, Pakistan.Dr. Haider Abbas Military College of Signals (MCS). NUST, Rawalpindi, Pakistan. Dr. I. M. Qureshi Air University, Islamabad, Pakistan.Dr. T.A. Cheema ISRA University Islamabad, Pakistan.Dr. M. Anwaar Saeed Virtual University of Pakistan. Pakistan.Dr. Adnan Aziz ISRA University Islamabad, Pakistan.Dr. M. Umair University of Central Punjab, Lahore, Pakistan.Dr. Shahid Naseem University of Sargodha, Lahore Campus.Dr. Tahir Naseem Ripha International University Lahore, Pakistan.Dr. Bilal Shoaib Global Institute Lahore, Pakistan.

LGURJCSIT

Mr. Natash Ali Mian Beacon House University Lahore, Pakistan.Engr. Amir Haider COMSATS Institute of Information Technology, Abbottabad, Pakistan.Mr. Muhammad Ahmad COMSATS Institute of Information Technology, Sahiwal, Pakistan.

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

LGURJCSIT

REVIEWERS COMMITTEEDr. Yousaf Saeed University of Haripur, Haripur, Pakistan.Dr. Sultan Ullah University of Haripur, Haripur, Pakistan.Dr. Shahzad Asif University of Engineering and Technology, Lahore, Pakistan.Dr. Atifa Athar COMSATS Institute of Information Technology, Lahore, Pakistan.Dr. Asim Qurtuba University of Science and Technology, Peshawar, Pakistan.Dr. Sharifullah Khan School of Electrical Engineering and Computer Science National University of Sciences and Technology (NUST).Dr. Kashif Zafar National University of Computer & Emerging Sciences Lahore, Pakistan.Dr. M. Aamer Saleem Ch. Hamdard University Islamabad, Pakistan.Dr. Tahir Naseem International Islamic University Islamabad, Pakistan.Dr. Umer Javeed International Islamic University Islamabad, Pakistan.Dr. Sajjad Ahmed Ghori International Islamic University Islamabad, Pakistan.Dr. A.N. Malik International Islamic University Islamabad, Pakistan.Dr. Haider Abbas Military College of Signals (MCS). NUST, Rawalpindi, Pakistan. Dr. I. M. Qureshi Air University, Islamabad, Pakistan.Dr. T.A. Cheema ISRA University Islamabad, Pakistan.Dr. M. Anwaar Saeed Virtual University of Pakistan. Pakistan.Dr. Adnan Aziz ISRA University Islamabad, Pakistan.Dr. M. Umair University of Central Punjab, Lahore, Pakistan.Dr. Shahid Naseem University of Sargodha, Lahore Campus.Dr. Tahir Naseem Ripha International University Lahore, Pakistan.Dr. Bilal Shoaib Global Institute Lahore, Pakistan.

LGURJCSIT

Mr. Natash Ali Mian Beacon House University Lahore, Pakistan.Engr. Amir Haider COMSATS Institute of Information Technology, Abbottabad, Pakistan.Mr. Muhammad Ahmad COMSATS Institute of Information Technology, Sahiwal, Pakistan.

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

Big Data and Precision Medicine

Haroon Ur Rashid1, Fatma Hussain2, Khalid Masood3

[email protected] , [email protected] , [email protected] Department of Computer Science, Lahore Garrison University, Lahore, Pakistan,

2 Department of Biochemistry, Faculty of Sciences, University of Agriculture, Faisalabad, Pakistan, 3 Faculty of Computing and IT, University of Jeddah, Jeddah, Kingdom of Saudi Arabia,

Abstract: This paper focuses on clinical data taken from diversified sources that can be utilized to predict medical conditions. Precision medicine being top priority in medication is main essence to describe treatment based on individual physiology, genetic makeup and other factors. Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital resources such as glucometers, insulin injectors, blood pressure monitors, and smart watches. Social media is an excellent source where people share their medical treatment status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created social media to prescribe medicines. Vital architectural components include storage programs (Amazon S3, Google cloud store), data incorporation mechanisms (Kafka, Storm Topology, Sqoop), APIs (Fitbit Web, Apple HealthKit, OneTouch, Facebook, Twitter), processing engine (Spark, Hadoop) and training datasets (Spark ML, Mahout scalable machine learning, data mining techniques, appropriate algorithms). Advantages of precision medicine includes powerful decision making resources (big data), better selection of disease targets, treatment opportunities, reduced medical expenses and timely delivery of healthcare. To optimize the capability of precision medicine, uninterrupted research funding, scientific initiatives, patient involvement in medicinal initiatives. Successful execution of precision medicine with holistic individually tailored approach necessitates the coordi-nated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management.

Key words: Genome, Biomarkers, Data Simulations, Healthcare Applications

01LGU R.J.Computer Science IT 2(1) LGURJCSIT MS ID-001 (2018)

LGU (RJCSIT)ISSN: 2519-7991

LGU R Journal forComputer Science IT

LGURJCSITISSN: 2519-7991

Research Article

Rashid et al LGURJCSIT 2018

Vol. 2 issue 1 Jan. March 2018

02 LGU R.J.Computer Science IT 2(1) LGURJCSIT MS ID-001 (2018)

Big Data and Precision Medicine

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of healthcare

Fig. 2:- Time is right option to implement precision medicine due to availability of

sequenced human genome, advanced research technologies and tools for analyzing data.

Crowdsourcing is an unconventional method that generates a rare model to evaluate precision medication. It excludes the inconsistent character of existing imaging data setup and accentuate to the amazing dimensions of big data in the cloud. Although, big data is quite variable about the data hierarchy always starts with computational imaging and infinite kinds of healthcare data. Any individual can add personnel data to accumulated data while maintaining privacy. Precision medicine can swiftly adopt cloud computing technologies, ultimately generalizing big data to decide right medicine at right time [5]. Human genome project, proteome project, computational bioinformatics and big data have contributed significantly in personalized medication. Omics and bioengineering are applied in identification, management and risk assessment of cancer. Expertise of healthcare providers are also enhanced by inclusion of advanced Omic techniques and molecular signatures in curricula.

Moreover, disparity in data among different ethnic groups are more visible than before. This difference plays significant role in future tailor-made therapeutic approaches [6]. Successful execution of precision medicine with holistic tailored based approaches necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management [7].

3. Data Sources

Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital

resources such as glucometers, insulin injectors, blood pressure monitors and smart watches. Social media is an excellent source when people share their medical treatment

status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created from above-mentioned data sources to prescribe personalized medicines [4].

re-incidence chances and complete cure varies among individuals. Cerebrovascular health is dependent upon guiding rules of precision medicine [12]. Precision nursing can take advantage from individual knowledge translations. Nursing science can examine genetic profiling, disease course, treatment outcomes and can assist in decision-making. Nurse scientists must collaborate with all disciplines to optimize care delivery while maintaining ethical norms [13]. Similarly, in pediatric rheumatology, informative data generated by scientific research is combined with innovative technology tools to elucidate pathophysiology. This practice aids in distinguishing subclasses of disease to support prognosis and treatment [14].

Parkinson's disease (PD) is the most prevalent neurodegenerative disease. It can neither be prevented nor cured. Although life-long treatment can minimize its deleterious effects. Late stages of PD are more devastating for the patient. Bu et al., [15] stated that advanced computer tools (omics, imaging, brain stimulation, wearable sensors) interpret

patient’s data from multiple perspectives and predict economical personalized medicine with fewer harmful effects. Hampel et al., hypothesized that efficacious recognition of precision medicine in Alzheimer’s disease and other neuropathological manifestations will revolutionize therapeutic selections [16]. Numerous mathematical models are used to evaluate big data and to simulate biological system behaviors. For example novel biomarkers of metabolic syndrome, insulin resistance, non-alcoholic fatty liver disease, nonalcoholic steato-hepatitis and carcinoma are identified by computational biology [17].Psychiatrics have yet not adopted the radical diagnosis and treatment expertise. Recently, Fernandes et al., [18] introduced a new discipline of precision psychiatry that can lessen the disease translation gaps and renovate the psychiatric medicine settings. Application of rules, algorithms, reference databases, big data analytics, IT technologies enable actionable decision, patient support and effective care (figure. 3)

To optimize the capability of precision medicine, it is obligatory to

1. Provide uninterrupted research funding Support scientific initiatives

2. Encourage patient involvement in medicinal initiatives

3. Create and train healthcare human resources

4. Establish and maintain precision deterrence activities

4. Application of Precision Medicine

Cancer diagnosis and treatment are improved by extensive insight gained through system biology. Identification of mutational resistance and drug targets involve mutation hotspots, model assimilations, UV signatures and genome analysis. Precision insights have the capability to recognize biochemical

mechanisms appropriate for rational drug targets [8]. According to Johnson [9], oncology research is dependent on precision medicine. Discovery of target drugs, improvement in laboratory skills, efficient record keeping can help forecast precision medicine. Genomic data commons (GDC) system is used to collect, examine and share cancer patients’ record. Large scale omics data in oncology hinders decision making ability in identifying malignant tumor genome and response. GDC facilitates access to cancer genomic records and supports the precision medicine efforts to identify and treat cancer [10]. Exploration of precision medicine, growth of sequencing methods and big data arising from clinical research has also established the future landscape for breast cancer treatment [11].

Precision medicine holds great potentials for stroke neurology. The identification, pathophysiology, progression, treatment,

Fig 1:- Black Diagram of Big Data & Precise medicine

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless processed to draw meaningful information to accelerate clinical practice. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine also but the choice of machine-learning algorithms is very crucial as shown in Figure 2. Unique

genotypes and phenotypes are explored with AI, thus help in improving patient care and reduce cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google cloud store.

[B]. Data incorporation mechanisms that carry real time and bulky data into the storage places following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web API (to collect Fitbit activity tracker data),

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments but it is within our capacity to treat group of patients with similar biomarkers in a precise way. Recently, Song

and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

In precision medicine, genetic data is manipulated to estimate disease risk and treatment strategies. Speedy expansion in health care records put the physician under immense pressure to develop effective treatment options. Large data sets or big data is useless unless not processed to draw meaningful information to accelerate clinical practices. The emergence of artificial intelligence (AI) has shortened the data processing time and quality of patient care. Applying AI in medicine, it becomes possible now to diagnose disease early with algorithms using numerous biomarkers, imaging documents, published research and electronic health records [2]. AI simulates human thinking, learning and information storage processes. It has great potential in precision cardiac medicine but choice of machine-learning algorithms are very crucial

as shown in Figure 2. Unique genotypes and phenotypes are explored with AI, thus help in improving patient care as well as reducinge cost and mortality rate [3]. Following vital architectural components are required to construct analytics for precision medicine [4]:

[A]. Storage programs where large data sets can be placed and accessed upon requisite. For example Amazon S3, Google Ccloud Sstore.

[B]. Data incorporation mechanisms carriesy real time and bulky data storage places through following Lambda Architectural patterns. Some important tools for this purpose include Kafka, Storm Topology, Sqoop and file ingestion APIs.

[C]. APIs extracts data from nonconventional traditional sources. For example Fitbit Web

Big Data and Precision Medicine

03LGU R.J.Computer Science IT 2(1) LGURJCSIT MS ID-001 (2018)

1. Introduction:

Precision medicine is a supercilious objective treated as the top priority in medication. The main essence is to describe treatment based on individual physiology, genetic makeup and other factors. It is a hectic work to attain personalized treatments, but it is within our capacity to treat group of patients with similar biomarkers in a

precise way. Recently, Song and Hu [1] stated that successful accomplishment of the Human Genome Project and precision medicine initiative by U.S. government were the most extraordinary events in human history that led to the emergence of precision medicine. Precision medicine means harnessing the biological, medicinal, epidemiological, statistical and social data to computer science techniques as shown in Figure 1

API (to collect Fitbit activity tracker data), Apple HealthKit API (to access health data from Apple watch, iPhone or other iOS devices), OneTouch Reveal API (to extract diabetes data from OneTouch glucometer devices), Facebook API or Twitter API (to obtain data from social media posts).

[D]. Processing engine can process big data ingested into the analytics platform. For example Spark and Hadoop framework.

[E]. Training datasets development to generate statistical models for diverse healthcare settings (age, gender, ethnic disparities). For example Spark ML or Mahout scalable machine learning, data mining techniques are used to create models.

2. Advantages of Precision Medicine

• Powerful decision making resources (big data)• Best selection of target diseases • Better treatment opportunities• Reduction in medical expenses• Timely delivery of hea