Back to 2008 this is the presentation of the master thesis titled "Pathway discovery in cancer: the Bayesian approach". In this thesis I focused on ovarian cancer microarray data analysis to predict gene-gene interactions using supervised learning and probabilistic methods.

1. Pathway Discovery in Cancer:
the Bayesian Approach
Francesco Gadaleta
Developed and written at ESAT dept. of Electrical Engineering of the Faculty of Engineering
Katholieke Universiteit Leuven (Belgium)

2. Genes and Deseases
Biological Assumptions
• Cancer normally originate in a single cell
• Cell’s life is regulated by many genes activated in different steps
Types Of Genes
• Oncogene
• Tumor-suppressor
• DNA-repair

3. Genes and Deseases:
genetic predisposition

4. Genes and Deseases:
genetic predisposition
Normal cell
First mutation Second mutation Third mutation
Malignant Cell

5. Genes and Deseases:
Microarray Technology
Cancer Cells
Red Fluorescent Probes
mRNA cDNA
Reverse
RNA Isolation Transcriptase Combine Target
Normal Cells Labeling
mRNA cDNA
Green Fluorescent Probes

6. Genes and Deseases:
the goal of biologists and genetists
• Prenatal diagnosis for recognized deseases, eg Down Sindrome
• Carrier testing to help couples with hereditary desease in the risky
decision of breeding
• Patient tailored diagnosis for genetic deseases

7. Goal of this Thesis
• Microarray Analysis by more complex tools
• Integrate in a unique model what is already known from other
experiments
• Identify those genes that form desease pathways

8. Type Of Data
• Normalization (fluorescent intensity)
• Filtering of microarray data (how to select subsets of genes)
• Data Discretization (Are bio reactions discrete events?)
➡ Interval discretization
➡ Quantile discretization
➡ Exporting

9. Interval Discretization
• Sort n observations
• Divide observations in d levels (uniformly spaced intervals)
• i-th obs. is disretized as j-th level iff:
x0 + j(xn-1 - x0) (j+1)(xn-1 - x0)
d < xi < x0 + d

10. Quantile Discretization
• Sort n observations
• Divide all observations in d levels by placing an equal number of obs.
in each bin: all levels are equally represented
• i-th observation belongs to j-th level iff:
jn (j+1)n
d <i< d

11. Exporting Data
Gene Name Sample_0 Sample_1 Sample_2 Sample_3 ... Sample_n
rpoH 29.345 30.431 25.125 29.543 29.987
mopA 42.746 40.375 41.740 29.345 29.345
htpG 29.345 29.345 29.345 29.345 29.345
...
araE 29.345 29.345 29.345 29.345 29.345

12. Knowledge Base
• Many biological processes are still not known
• Reliabiality of data
➡ hybridization is still a handmade process
• Small sample size - Huge number of genes
➡ integration with heterogeneous data

13. What we want to solve?
• Genetical cancer forecasting?
• Need for a model to handle uncertain knowledge
• A model that biologists and epidemiologists can understand
• A model to be updated in different times

14. What we want to solve?
• Need for a model to handle uncertain knowledge
• A model that biologists and epidemiologists can understand
• A model to be updated in different times

15. Bayesian Networks:
features
• Can handle uncertain knowledge with probability
• Can handle subsequent changes (bio noise, multiple measurements)
• Intuitive model a biologist can understand: white box vs. black box
(neural networks)

16. Bayesian Networks:
definition
• Direct Acyclic Graph
(how variables interact each other)
A B
C
• Set of local probability distributions F
(p(xi=k | Pa(xi)=j) = ijk)
E D
G

17. Bayesian Networks:
definition
•
p(A)
Direct Acyclic Graph A B
(how variables interact each other)
C
• Set of local probability distributions F
(p(xi=k | Pa(xi)=j) = ijk)
E D
G

18. Bayesian Networks:
definition
•
p(A) p(B)
Direct Acyclic Graph A B
(how variables interact each other)
C
• Set of local probability distributions F
(p(xi=k | Pa(xi)=j) = ijk)
E D
G

19. Bayesian Networks:
definition
•
p(A) p(B)
Direct Acyclic Graph A B
(how variables interact each other)
p(C|A,B)
C
• Set of local probability distributions F
(p(xi=k | Pa(xi)=j) = ijk)
E D
G

20. Bayesian Networks:
definition
•
p(A) p(B)
Direct Acyclic Graph A B
(how variables interact each other)
p(C|A,B)
C
• Set of local probability distributions F p(F|B)
(p(xi=k | Pa(xi)=j) = ijk)
E D
G

21. Bayesian Networks:
definition
•
p(A) p(B)
Direct Acyclic Graph A B
(how variables interact each other)
p(C|A,B)
C
• Set of local probability distributions F p(F|B)
(p(xi=k | Pa(xi)=j) = ijk)
p(E|C)
E D
G

22. Bayesian Networks:
definition
•
p(A) p(B)
Direct Acyclic Graph A B
(how variables interact each other)
p(C|A,B)
C
• Set of local probability distributions F p(F|B)
(p(xi=k | Pa(xi)=j) = ijk)
p(E|C) p(D|C)
E D
G

23. Bayesian Networks:
definition
•
p(A) p(B)
Direct Acyclic Graph A B
(how variables interact each other)
p(C|A,B)
C
• Set of local probability distributions F p(F|B)
(p(xi=k | Pa(xi)=j) = ijk)
p(E|C) p(D|C)
E D
p(G|F)
G

24. Bayesian Networks:
formal assumptions
• Structure Possibility
• Complete Data
• Markov Condition
• Observational Equivalence
• Scoring Function

25. Bayesian Networks:
formal assumptions
• Structure Possibility Each of the n! structures is possible
• Complete Data p(Si |) 0
• Markov Condition
• Observational Equivalence
• Scoring Function

26. Bayesian Networks:
formal assumptions
• Structure Possibility
• Complete Data
• Markov Condition
• Observational Equivalence
• Scoring Function

27. Bayesian Networks:
formal assumptions
• Structure Possibility No missing data in order to compute
• Complete Data p(S, S|) and p(C|D, S, ),
• Markov Condition C new observation,
in closed form
• Observational Equivalence
• Scoring Function

28. Bayesian Networks:
formal assumptions
• Structure Possibility
• Complete Data
• Markov Condition
• Observational Equivalence
• Scoring Function

29. Bayesian Networks:
formal assumptions
• Structure Possibility Allows to factorize
• Complete Data p(x1 , x2 , . . . xn ) = p(xi |P a(xi ))
• Markov Condition
• Observational Equivalence
• Scoring Function

30. Bayesian Networks:
formal assumptions
• Structure Possibility
• Complete Data
• Markov Condition
• Observational Equivalence
• Scoring Function

31. Bayesian Networks:
formal assumptions
X1 X1
• Structure Possibility
• Complete Data X2 X3 X2 X3
• Markov Condition X4 X4
• Observational Equivalence
• Scoring Function X5 X5

32. Bayesian Networks:
formal assumptions
• Structure Possibility
• Complete Data
• Markov Condition
• Observational Equivalence
• Scoring Function

33. Bayesian Networks:
formal assumptions
• Structure Possibility A function to measure how well a
• Complete Data structure fits the data
• Markov Condition
• Observational Equivalence
• Scoring Function

34. Bayesian Networks:
formal assumptions
• Structure Possibility
• Complete Data
• Markov Condition
• Observational Equivalence
• Scoring Function

35. Bayesian Network:
structure learning
• Constraint Satisfaction Problem vs. Optimization Problem
• CSP tries to discover dependencies from the data with a statistical
hypothesis test
• OP searches and tries to improve the score assigned by a scoring
function

36. Bayesian Networks:
K2 algorithm
• Goal: maximize the structure probability given the data
• A initial order is given (A,B,C, D, E, F, G)
[Quality measure of the
net given the data by
Cooper Herskovits]

37. Bayesian Networks:
K2 algorithm
[Quality measure of the
net given the data by
Cooper Herskovits]

38. Bayesian Networks:
K2 algorithm
• let D the dataset, N the number of examples,
• G the network structure, paij the j th instantiation of P a(xi ),
• Nijk the number of data where xi = k and P a(xi ) = j, and
ri
• Nij = k=1 Nijk
[Quality measure of the
net given the data by
Cooper Herskovits]

39. Bayesian Networks:
K2 algorithm
• let D the dataset, N the number of examples,
• G the network structure, paij the j th instantiation of P a(xi ),
• Nijk the number of data where xi = k and P a(xi ) = j, and
ri
• Nij = k=1 Nijk
P (G, D) = P (G)P (D|G) [Quality measure of the
n qi ri net given the data by
(ri −1)!
P (D|G) = i=1 j=1 (Nij +ri −1)! k=1 Nijk ! Cooper Herskovits]

40. Bayesian Networks:
K2 algorithm
• Possible actions
• edge addition
• edge deletion

41. Data Integration
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

42. Data Integration
• heterogeneous data integration
Gene1
Gene2
Gene3 G1 G9
Gene4
. G7
GeneN
G5
• binary gene-gene relations Literature extraction
Fixed vocabulary
G3
G4
G2
prior G8
Abstract
Indexing
prior
• bayesian network collective learning
Cosine measure
(Partial Integration) prior
Gene
Similarity
Matrix

43. Data Integration
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

44. Data Integration
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

45. Data Integration
Microarray data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

46. Data Integration
Microarray data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

47. Data Integration
Microarray data Clinical data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

48. Data Integration
Microarray data Clinical data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

49. Data Integration
Microarray data Clinical data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

50. Data Integration
Microarray data Clinical data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

51. Data Integration
Microarray data Clinical data
• heterogeneous data integration
• binary gene-gene relations
• bayesian network collective learning
(Partial Integration)

52. Experiments and results
a generator of synthetic gene expression data
SynTReN for design and analysis of structure learning
algorithms
syntetic model syntetic data
Validator
Structure
Learning
Framework
learned model

53. Experiments and results
• Results (random net + bio net (without clinical data))
• Idea that clinical data may improve structure learning: more complete
biological models (not bad considering that it is a type of data medical centers are
equipped)

54. Learned Structure Network
Microarray variables
232
DEMO
Clinical variables
11
Patients
(train) 78
Patients
(test) 19
Structure Learning
Computation time 12h (*)
(*) Matlab running on Intel 2CoreDuo 2Ghz

55. Conclusions
• Partial Integration of two data sources improves performance within
the Bayesian Network Framework
• A huge pure-microarray dataset is not helpful
• Data Integration leads to fewer variables for each source (pure
microarray is expensive)