Kansas State University Department of Computing and Information Sciences Bioinformatics and Machine Learning: Bioinformatics and Machine Learning: Building Probabilistic Models Building Probabilistic Models of Gene Expression from Microarray of Gene Expression from Microarray Data Data William H. Hsu with Haipeng Guo, Rengakrishnan Subramanian, Ben Perry, and Julie A. Thornton Department of Computing and Information Sciences Kansas State University Laboratory for Knowledge Discovery in Databases http:// www.kddresearch.org/Groups/Bioinformatics
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William H. Hsu with Haipeng Guo, Rengakrishnan Subramanian, Ben Perry, and Julie A. Thornton
Bioinformatics and Machine Learning: Building Probabilistic Models of Gene Expression from Microarray Data. William H. Hsu with Haipeng Guo, Rengakrishnan Subramanian, Ben Perry, and Julie A. Thornton Department of Computing and Information Sciences Kansas State University - PowerPoint PPT Presentation
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Kansas State University
Department of Computing and Information Sciences
Bioinformatics and Machine Learning:Bioinformatics and Machine Learning:Building Probabilistic ModelsBuilding Probabilistic Models
of Gene Expression from Microarray Dataof Gene Expression from Microarray Data
William H. Hsu
with Haipeng Guo, Rengakrishnan Subramanian,
Ben Perry, and Julie A. Thornton
Department of Computing and Information Sciences
Kansas State UniversityLaboratory for Knowledge Discovery in Databases
http://www.kddresearch.org/Groups/Bioinformatics
Kansas State University
Department of Computing and Information Sciences
OverviewOverview
• Computer Science: What We Do– Software: operating systems, programming languages, software
engineering, databases
– Hardware: logic design, organization and architecture
– Theory of Computation: algorithms, complexity, languages
Genetic Algorithms for Parameter Tuning in Genetic Algorithms for Parameter Tuning in Bayesian Network Structure Learning [2]Bayesian Network Structure Learning [2]
Kansas State University
Department of Computing and Information Sciences
LearningEnvironment
Specification Fitness(Inferential Loss)
[B] ParameterEstimation
[A] StructureLearning
G = (V, E)Graph Component of BN
D: Microarray Data
B = (V, E, )BN with Probabilities
Dval (Model Validation by Inference)
G1
G2
G3
G4 G5
G1
G2
G3
G4 G5
Kansas State University
Department of Computing and Information Sciences
MicroarraysMicroarrays
Kansas State University
Department of Computing and Information Sciences
A Gene Network for YeastA Gene Network for Yeast[Friedman, Nachman, Linial, Pe’er, 2000][Friedman, Nachman, Linial, Pe’er, 2000]
Kansas State University
Department of Computing and Information Sciences
Publication(e.g., PubMed)
Source(e.g.,
Taxonomy)
Gene(e.g., GenBank)
Experiment
Sample Hybridization Array
Normalization/Discretization
Data
Components of A Microarray Experiment:Components of A Microarray Experiment:HybridizationHybridization
Kansas State University
Department of Computing and Information Sciences
ComputationalWorkflows
(e.g., myGrid)
ExperimentalServices &Metadata
(Mage-ML XML)
GeneExpression
Model
Pathway &NetworkLearning
Specification
DataPreprocessingSpecification
ParameterLearning
Specification
ModelAnalysis
Specification
DiscretizationUse Case
Data MiningUse Case
Feature Selection
Specification
Validation(e.g., Bootstrap)
Use Case
Components of A Microarray Experiment:Components of A Microarray Experiment:Computational Gene Expression ModelingComputational Gene Expression Modeling
Kansas State University
Department of Computing and Information Sciences
Domain-Specific Repositories
Experimental DataSource Codes and Specifications
Data ModelsOntologies
Models
DESCRIBER
Personalized Interface
Domain-SpecificCollaborative Filtering
New QueriesLearning and Inference
Components
HistoricalUse Case & Query Data
Decision SupportModels
Users ofScientificDocumentRepository
Interface(s) to Distributed Repository
Example Queries:• What experiments have found cell cycle-regulated
metabolic pathways in Saccharomyces?
• What codes and microarray data were used, and why?
DESCRIBERDESCRIBER: An Experimental: An ExperimentalIntelligent FilterIntelligent Filter