Download Machine learning approaches for biological data analysis

Machine learning approaches for biological
data analysis
Jian Peng
Department of Computer Science
University of Illinois at Urbana-Champaign
Biological data
Graphical “search” engine for drug discovery
Cell type
Pathway
on/off
Drug
interaction
membership
Protein
perturbation
association
Side
effect
association
association
Disease
Mutation
association
Network analysis
Example: predicting gene function
Gene Vector Space
Molecular networks
z
Gene 5
DCA
x
BP edge
BP edge
x
GO 2
Gene 3
GO 3
predict
Unobserved annotations
Gene 3
GO 2
GO 3
Gene 1
GO 1
y
Sibling relationship
High Dimensional Space
Gene 3
GO 1
Project y’=wx
z
GO 2
Gene 5
Gene 2
project matrix w
Function Vector Space
DCA
Gene 1
train
Gene 2
y
Gene Ontology
observed annotations
Gene 3
Gene 4
GO 1
Low Dimensional Space
Example: drug target prediction
network analysis
new disease biology
(potential drug targets)
human disease network
Probabilistic graphical models for drug discovery
Cell type
Pathway
on/off
Drug
interaction
membership
Protein
perturbation
association
Side
effect
association
association
Disease
Mutation
association
Efficient inference
• Discriminance sampling for partition function estimation
Sampling
Classification
Restricted Boltzmann Machine
Deep Boltzmann Machine
• Combining variational inference and sampling approaches