Learning disjunctions in Geronimo

1
Learning disjunctions in Geronimos regression
trees

• Felix Sanchez Garcia
• supervised by Prof. Dana Peer

2
Motivation

• Gliobastoma most common primary brain tumour in
  adults.
• Newly diagnosed patients have an average survival
  of 1 year.
• Need for better models of the network.
• Data used to create models microarrays

• genes ? 8000
• candidate regulators ? 800
• samples ?120

3
Module networks

• Bayesian model that benefits from high
  correlation of groups of variables 2
• Algorithm similar to EM (but hard decisions).
  Loop
• Module assignment step assign variables to
  modules
• Structure search step calculate CPD for each
  module

4
Regression trees as CPD
xlt0.3

• Regression trees are used for each modules CPD
• Internal nodes condition on a single variable
• Leaf nodes parameters for normal distribution
• Bayesian score
• Exhaustively calculates score for each split for
  each regulator

ygt-0.2
pdf of normal-gamma
prior on structure (complexitybiological
penalties)
5
Incorporating pathway information

• Biological pathways contain sets of genes and
  represent chains of biochemical reactions that
  perform some function
• Aberrations in gliobastoma tend to occure as
  disjunctions within pathways derregulating 1
  component is usually enough to alter the function
  of the whole pathway 4
• Idea use pathway information to obtain a better
  model
• Methodology extend node conditions to
  disjunctions of conditions on pathway elements
• We will use 15 sets of regulators (20-30 genes
  per set)
• 5 sets of regulators of pathways known to be
  related to cancer.
• 5 sets of regulators of other pathways
• 5 sets of regulators chosed at random

6
Problem setting

• Concept class disjunction of threshold functions
  on a single variable
• Loss functions -Bayesian score (biological
  penalty?)
• Potential number of hypotheses 2m
• Related classification problem tackled by
  Marchand and Shah (2005) and Kestler et al.
  (2006).

7
Bibliography

• Pe'er, D., Bayesian Network Analysis of Signaling
  Networks A Primer. Sci. STKE, 2005. 2005(281)
  p. pl4-.
• Segal, E., et al., Module networks identifying
  regulatory modules and their condition-specific
  regulators from gene expression data. Nat Genet,
  2003. 34(2) p. 166-176.
• Lee, S.-I., et al., Identifying regulatory
  mechanisms using individual variation reveals key
  role for chromatin modification. Proceedings of
  the National Academy of Sciences, 2006. 103(38)
  p. 14062-14067.
• Comprehensive genomic characterization defines
  human glioblastoma genes and core pathways.
  Nature, 2008. 455(7216) p. 1061-1068.
• Kestler, H., W. Lindner, and A. Müller, Learning
  and Feature Selection Using the Set Covering
  Machine with Data-Dependent Rays on Gene
  Expression Profiles, in Artificial Neural
  Networks in Pattern Recognition. 2006. p.
  286-297.
• Marchand, M. and M. Shah, PAC-Bayes Learning of
  Conjunctions and Classification of
  Gene-Expression Data, in Advances in Neural
  Information Processing Systems 17. 2005, MIT
  Press Cambridge, MA. p. 881-888.