Analysing Microarray Data Using
Bayesian Network Learning
Name: Phirun Son
Supervisor: Dr. Lin Liu
Contents
Aims
 Microarrays
 Bayesian Networks
 Classification
 Methodology
 Results

Aims and Goals
Investigate suitability of Bayesian
Networks for analysis of Microarray data
 Apply Bayesian learning on Microarray
data for classification
 Comparison with other classification
techniques

Microarrays
Array of microscopic
dots representing gene
expression levels
 Gene expression is the
process of DNA genes
being transcribed into
RNA
 Short sections of genes
attached to a surface
such as glass or silicon
 Treated with dyes to
obtain expression level

Challenges of Microarray Data



Very large number of
variables, low number
of samples
Data is noisy and
incomplete
Standardisation of data
format
◦ MGED – MIAME,
MAGE-ML, MAGE-TAB
◦ ArrayExpress, GEO,
CIBEX
Bayesian Networks
Represents conditional independencies of
random variables
 Two components:

◦ Directed Acyclic Graph (DAG)
◦ Probability Table
Methodology

Create a program to test accuracy of
classification
◦ Written in MATLAB using Bayes Net Toolbox (Murphy, 2001),
and Structure Learning Package (Leray, 2004)
◦ Uses Naive network structure, K2 structure learning, and predetermined structure
Test program on synthetic data
 Test program using real data
 Comparison of Bayes Net and Decision
Tree

Synthetic Data

Data created from well-known Bayesian
Network examples
◦ Asia network, car network, and alarm
network
Samples generated from each network
 Tested with naive, pre-known structure,
and with structure learning

Synthetic Data - Results
50 Samples, 10 Folds, 100
Iterations
Class Node: Dyspnoea
Correct
Naive
81.0%
K2 Learning
83.4%
Known Graph
85.0%
100 Samples, 10 Folds, 50
Iterations
Class Node: Dyspnoea
Correct
Naive
83.1%
K2 Learning
84.3%
Known Graph
85.1%
Asia Network
Lauritzen and Spiegelhalter, ‘Local Computations with
Probabilities on Graphical Structures and Their Application to
Expert Systems’, 1988, pg 164
Synthetic Data - Results
50 Samples, 10 Folds, 100
Iterations
Class Node: Engine Starts
Correct
Naive
53.5%
K2 Learning
58.3%
Known Graph
62.4%
100 Samples, 10 Folds, 50
Iterations
Class Node: Engine Starts
Correct
Naive
56.5%
K2 Learning
58.7%
Known Graph
61.2%
Car Network
Heckerman, et al, ‘Troubleshooting under Uncertainty’, 1994
pg 13
Synthetic Data - Results
50 Samples, 10 Folds, 10 Iterations
Class Node: InsufAnesth
Correct
Naive
72.4%
K2 Learning
78.7%
ALARM Network
Known Graph
89.6%
37 Nodes, 46 Connections
50 Samples, 10 Folds, 10 Iterations
Class Node: Hypovolemia
Correct
Naive
69.0%
K2 Learning
77.8%
Known Graph
93.6%
Beinlich et al, ‘The ALARM monitoring system: A case study
with two probabilistic inference techniques for belief
networks’, 1989
Lung Cancer Data Set

Publically available data sets:
◦ Harvard: Bhattacharjee et al, ‘Classification of Human Lung Carcinomas
by mRNA Expression Profiling Reveals Distinct Adenocarcinoma
Subclasses’, 2001
 11,657 attributes, 156 instances, Affymetrix
◦ Michigan: Beer et al, ‘Gene-Expression Profiles Predict Survival of
Patients with Lung Adenocarcinoma’, 2002
 6,357 attributes, 96 instances, Affymetrix
◦ Stanford: Garber et al, ‘Diversity of Gene Expression in Adenocarcinoma
of the Lung’, 2001
 11,985 attributes, 46 instances, cDNA
 Contains missing values
Feature Selection

Li (2009) provides a feature-selected set
of 90 attributes
◦ Using WEKA feature selection
◦ Also allows comparison with Decision Tree
based classification

Discretised data in 3 forms
◦ Undetermined values left unknown
◦ Undetermined values put into either category – two category
◦ Undetermined values put into another category – three category
WEKA: Ian H. Witten and Eibe Frank, ‘Data Mining: Practical machine learning
tools and techniques’, 2005.
Harvard Set


Harvard Training on Michigan
MATLAB
WEKA
DT
2-Cat -> 2-Cat NF
95 (99.0%)
95 (99.0%)
95 (99.0%)
2-Cat -> 2-Cat F
94 (97.9%)
93 (96.9%)
92 (95.8%)
3-Cat -> 3-Cat NF
94 (97.9%)
95 (99.0%)
94 (97.9%)
3-Cat -> 3-Cat F
88 (91.7%)
95 (99.0%)
94 (97.9%)
MATLAB
WEKA
DT
2-Cat -> 2-Cat NF
41 (89.1%)
46 (100%)
43 (93.5%)
2-Cat -> 2-Cat F
41 (89.1%)
45 (97.8%)
36 (78.3%)
3-Cat -> 3-Cat NF
41 (89.1%)
46 (100%)
42 (91.3%)
3-Cat -> 3-Cat F
41 (89.1%)
46 (100%)
42 (91.3%)
Harvard Training on Stanford
Michigan Set


Michigan Training on Harvard
MATLAB
WEKA
DT
2-Cat -> 2-Cat NF
150 (96.2%)
154 (98.7%)
153 (98.1%)
2-Cat -> 2-Cat F
144 (92.3%)
153 (98.1%)
150 (96.2%)
3-Cat -> 3-Cat NF
145 (92.9%)
153 (98.1%)
153 (98.1%)
3-Cat -> 3-Cat F
140 (89.7%)
152 (97.4%)
153 (98.1%)
Michigan Training on Stanford
MATLAB
WEKA
DT
2-Cat -> 2-Cat NF
41 (89.1%)
46 (100%)
41 (89.1%)
2-Cat -> 2-Cat F
41 (89.1%)
46 (100%)
40 (87.0%)
3-Cat -> 3-Cat NF
41 (89.1%)
45 (97.8%)
39 (84.8%)
3-Cat -> 3-Cat F
41 (89.1%)
46 (100%)
39 (84.8%)
Stanford Set


Stanford Training on Harvard
MATLAB
WEKA
DT
2-Cat -> 2-Cat NF
139 (89.1%)
153 (98.1%)
139 (89.1%)
2-Cat -> 2-Cat F
139 (89.1%)
150 (96.2%)
124 (79.5%)
3-Cat -> 3-Cat NF
139 (89.1%)
150 (96.2%)
154 (98.7%)
3-Cat -> 3-Cat F
139 (89.1%)
150 (96.2%)
152 (97.4%)
Stanford Training on Michigan
MATLAB
WEKA
DT
2-Cat -> 2-Cat NF
86 (89.6%)
95 (99.0%)
86 (89.6%)
2-Cat -> 2-Cat F
86 (89.6%)
92 (95.8%)
72 (75.0%)
3-Cat -> 3-Cat NF
86 (89.6%)
95 (99.0%)
94 (97.9%)
3-Cat -> 3-Cat F
86 (89.6%)
95 (99.0%)
91 (94.8%)
Future Work
Use structure learning for Bayesian
Classifiers
 Increase of homogeneous data
 Other methods of classification
