Jurisica Slides - Rutgers University

Avoiding Paralysis of Analysis:

Building an Intellectual Prosthesis

Knowledge-Oriented Analysis of Mycroarray Data

I. Jurisica

DIMACS'01 I. Jurisica 1

Goals

Parallel analysis of gene expressionsImproved understanding of tumorigenesisTumor classification

Individualized medicineImproved diagnosis, prognostics, treatment planning & adjustmentTargetted therapy &drug design/useInformed patient


Problems

Multi-dimensionalitymany degrees of freedom, few datapoints

NoiseImprecision, variationLow number of repeats

Non-independebilityNon-linearityDBs changeIntegration of results with other DBs & multiple experiments


Intellectual Prosthesis

Fixed

Parametric

Nonparametric

Nonparametricwith Processing

Mo

re K

no

wle

dg

e

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e D

ata

Finding appropriate model to support reasoning

Exceptions

Evolution


AnalysisClustering organizes observations into groups by max. iner-cluster and min. inter-cluster similarityClassification/prediction assigns an observation to a class (finite/infinite)Comparison describes the item by comparing it to other itemsSummarization describes common characteristics of a subsetDiscrimination describes minimum features needed to differentiate among classesAssociation finds common occurrence of observations


Paralysis

Sourcetoo slow to search the problem spacenot enough data/processing time available for a system to generate a NP modellack of domain knowledgetoo much data (including noise) from HTP (high dimensionality)

A solutionHTP & computationGenerate - analyze - reduce - test - validate


HTP

Modified CBR approachsymbolic similaritylazy learning combined with

clustering & classificationsummarization

Analysis-based researchDNA microarray analysisannotation

RememberingRetrievingReasoning


Model-Building Solutions

Eager approach1. analyze data2. create a model3. use the modelLazy approach - data-driven model1. incrementally accumulate data2. incrementally analyze & evolve

Generate - analyze - reduce - test - validate

Exceptions

Evolution


Analyzing and Using MA DataProblems

Knowledge of classesProviding parametersClinical attributes as measures of "meaningfulness"ScalabilityAnnotating and explaining resultsQuality assuranceIntegratability


Discovery Algorithms

http://cmgm.stanford.edu/pbrown/

www.partek.com



Case-Based ReasoningSOLUTION

1. Diagnosis2. Prognosis3. Treatment planGeneral Demographics

& Medical HistoryClinical Presentation

& Prognostic FactorsSurgical DetailsPathology StagingClinical StagingResearch ProtocolFollow-upAgeDatesHematologyBiochemistry19.2k expression profiles, .... Store

ReasonAnalyze


Case-Based ReasoningDSS

Cases represent experiential knowledgeCases are patterns: context, problem, solutionSymbolic similarity - context-basedRetrieval - k-NN with context and structureAnytime algorithm

KM for evolving domainsDocumenting, analyzing,transferring & sharing experienceClassification, prediction,guidance in hypothesis discoveryClustering, summarizationAcquire now, process later

RememberingRetrievingReasoning


Patient Information Management

we need detailed disease classificationwe need markers to improve diagnosis, prognosis and treatment planingwe need new and systematic methods


CBR for DNA Micro Arrays

Gene expression signatureFind patients with similar signature

k-NN approach - without prior domain knowledge

Provide diagnosis, prognosis & treatment by analogyApply Explain function for marker & cancer subtype summarization


Advantage of CBR

Supports reasoning, not just analysisMeasure of similarity is based on gene expression profileDoes not require prior knowledgeSupports evolution & is more flexibleHandles inconsistencies

Inconsistencies get resolved at run-time with contextual informationCBR can be used to find inconsistencies

Supports discovery & validationDIMACS'01 I. Jurisica 16

Outliers

Represent change and deviationdata outside of normal region of input

unusual but correctunusual & incorrect

for numeric attributesdetect with histogram

remove with threshold filteridentify by calculating the mean & stdev

remove by specifying "window", e.g., 2 standard deviations from the mean


KD and CBR

Patients

Gen

es &

clin

ical

att

ribut

es

Genes

Pat

ient

s

Organize genes into groupsOrganize attribute values into taxonomies

Clinical


Context Relaxation


Patient-Patient Similarity




Open Source BIOdb

Automated annotationSchema integration, info validationQuerying and analysisReasons for local source:

certain tasks are more efficient and effectivecertain tasks become possible


WebOQL

A system for supporting data restructuring operations

to integrate data from different sources (documents, relational tables, hypertexts)to restructure an instance of a given source into an instance of another one

We used WebOQL to write wrappers for UniGene

more generic, dynamic, incremental

http://www.cs.toronto.edu/~weboql


Autoannotations

Information may not be downloadableInformation may not be complete

ID=1TITLE=Hippocampus,_Stratagene_(cat.__936205)TISSUE=brain, hippocampus VECTOR=lambdaZAP-II

Lib.1Infant, 2 yrs, femalebrain, hippocampuslambdaZAP-II453 ESTs have been classified, 411 gene sets


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Expression Distribution


Lung 15,410Lung-tumor 67Lung-tumor & suppressor 26Lung-tumor & necrosis 20Lung-tumor & antigen 5Lung-tumor & susceptibility 3

Hs.241493 M. musculus PIR:B47328 B47328 natural killer cell tumor-recognition protein - mouse" 1511 79 %Hs.241493 H. sapiens SP:P30414 NKCR_HUMAN NK-TUMOR RECOGNITION PROTEIN" 1461 100 %Hs.19074 H. sapiens PID:g7212790 large tumor suppressor 2" 1045 100 %Hs.48499 H. sapiens PID:g7144644 AF102177 1 tumor antigen SLP-8p" 965 100 %Hs.116875 M. musculus PID:g7637845 AF172722 1 tumor-rejection antigen SART3" 962 87 %Hs.211600 M. musculus SP:Q60769 TNP3 MOUSE TUMOR NECROSIS FACTOR, ALPHA-INDUCED

PROTEIN 3"789 88 %

Hs.211600 H. sapiens SP:P21580 TNP3_HUMAN TUMOR NECROSIS FACTOR, ALPHA-INDUCED PROTEIN 3"

789 100 %

Lung


Conclusions

Management - representation - reasoning - discovery

moving from hypothesis-driven to exploration-driven research (analysis)systematically analyzing the problem space

HTPautomation, systematicity, reproducibilityhypothesis search - generation & evaluation


"Most disease processes and treatments are manifested at the protein level""Gene-based expression analysis alone will (in certain cases) be totally inadequate for drug discovery""Only 2% of diseases are believed to be monogenic - we need to understand protein-protein interactions"

The Future

DDT 4(3):129-133, 1999


Thanks

P. Rogers, M. SultanA. Rehaag, G. QuonD. Wigle, O. HunerP. Macgregor, M. Albert

J. Glasgow

NSERC, CITO,NIH, IBM, OCIA. Barta

M. MaziarzW. Andreopoulos

http://www.cs.utoronto.ca/~juris DIMACS'01 I. Jurisica 30

Jurisica Slides - Rutgers University

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