Isabelle Bichindaritz
University of Washington
Institute of Technology
Tacoma, WA, USA
Ecole des Hautes Etudes
en Santé Publique
Département Infobiostat
Rennes, France
Purpose of this Talk
 Once upon a time …
 There was biology (~1800), and
 There were computers (~1920)
 Of their common interests was born bioinformatics (~1979) …
 Question:
 How can CBR contribute to bioinformatics research ?
 An example to microarray data analysis
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NCBI, 2004
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Bioinformatics Challenges
 Frequent tasks in bioinformatics
 Similarity search in genetic sequences
 Microarray data analysis
 Macromolecule shape prediction
 Evolutionary tree construction
 Gene regulatory network mining
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Bioinformatics Challenges
 Microarray data analysis
 Microarrays are made from a collection of purified DNA’s. A drop
of each type of DNA in solution is placed onto a speciallyprepared glass microscope slide by an arraying machine.
 Please note that …
 … the human genome contains about 30,000 genes.
 … a microarray can contain thousands or tens of thousands
relatively short nucleotides of known sequences.
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Bioinformatics Challenges
 The end product of a comparative hybridization
experiment is a scanned array image.
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Bioinformatics Challenges
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Bioinformatics Challenges
Microarray applications
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Determine relative DNA levels associated with huge number
of known and predicted genes in a single experiment.
The most attractive application of microarrays is in the
study of differential gene expression in disease.
The up– or down-regulation of gene activity can either be
the cause of the pathophysiology or the result of the
disease.
Accurate measurement of every single gene is assessed.
Sensitivity: very high – detect the presence of one transcript
in one-tenth of a cell.
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Bioinformatics Challenges
 Data mining challenges
 Volume of data (Giga bytes, number of features)
 Characteristics of data (specific constraints)
 Domain specific knowledge (expert interpretation)
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BMA-CBR System
Gene Expression Level
Dataset
Application of Feature
Selection Algorithm
Discrete Sample
Output: Supervised
Machine Learning and
Model Construction
through Classification
Continuous Sample
Output: Supervised
Machine Lerning and
Model Construction
through Prediction
Diagnosis
Survival analysis
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BMA-CBR System
 BMA-CBR system performs feature selection through
BMA before using CBR for microarray data classification
and prediction (survival analysis)
 Introduction and motivation of variable selection
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What is Bayesian Model Averaging (BMA)?
One approach: the iterative BMA algorithm
Application 1: Chronic Myeloid Leukemia (CML)
Application 2: Survival analysis
 Presentation of CBR
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Bayesian Model Averaging
 Feature selection
 Used to select a subset of relevant features for building robust
learning models in machine learning.
 Often used in supervised learning.
 Select relevant features from the training set (for which class
labels are known).
 Apply the selected features in the test set.
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Bayesian Model Averaging
 Feature selection
 A minimal set of relevant genes for future prediction or assay
development
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Bayesian Model Averaging
 Typical variable selection methods – one variable at a
time
 Examples:
 T-test
 Between group to within group sum of squares (BSS/ WSS)
[Dudoit et al. 2001]
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Bayesian Model Averaging
 Multivariate gene selection
 Our goal: consider multiple genes
 Simultaneously to exploit the interdependence between genes
to reduce # relevant genes
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Bayesian Model Averaging
 Bayesian Model Averaging (BMA) [Raftery 1995],
[Hoeting et. al. 1999]
 A multivariate variable selection technique.
 Typical model selection approaches select a model and then
proceed as if the selected model has generated the data -->
overconfident inferences
 Advantages of BMA:
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Fewer selected genes
Can be generalized to any number of classes
Posterior probabilities for selected genes and selected models
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Bayesian Model Averaging
 BMA
 Average over predictions from several models
 What do we need?
 Prediction with a given model k --> logistic regression
 How to choose a set of “good” models? --> variable selection
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Bayesian Model Averaging
 What models to average over?
 All possible models --> way too many!!
 Eg. 2^30~1 billion, 2^50~10^15 etc…
 The BMA solution:
1. “leaps and bounds” [Furnival and Wilson 1974] : when
#variables (genes) <= 30, we can efficiently produce a
reduced set of good models (branch and bound).
2. Cut down the # models?
Discard models that are much less likely than the best
model.
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Bayesian Model Averaging
 Iterative BMA algorithm [Yeung, Bumgarner, Raftery
2005]
 Pre-processing step: Rank genes using BSS/WSS ratio.
 Initial step:
 Repeat until all genes are processed:
 Output: selected genes and models with their
posterior probabilities
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Bayesian Model Averaging
 Application 1: Classification of progression of
chronic myeloid leukemia (CML)
 Motivation: New Candidates for Prognostic
studies in CML
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Bayesian Model Averaging
 Progression of CML
 CML usually presents in chronic phase (CP), but in the absence
of effective therapy, CP CML invariably transforms to
accelerated phase (AP) disease, and then to an acute
leukemia, blast crisis (BC).
 BC is highly resistant to treatment, and all treatments are more
successful when administered during CP.
 Imatinib is most effective in early CP patients with excellent
survival (86% at 7 years).
 Currently there are limited clinical markers and no molecular
tests that can predict the “clock” of CML progression for
individual patients at the time of CP diagnosis, making it
difficult to adapt therapy to the risk level of each patient.
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Bayesian Model Averaging
Why predictors for CML progression?
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Bayesian Model Averaging
 Identification of CML progression biomarkers
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Bayesian Model Averaging
 Genes associated with CML progression
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Bayesian Model Averaging
 BMA selected genes using microarray data
 Selected 6 genes over 21 models
 Repeat CV 100 times
 Average Brier Score = 0.21
 Average prediction accuracy = 99.17%
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Bayesian Model Averaging
PCR data: CP-early vs CP-late
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Bayesian Model Averaging
 Summary: CML data
 BMA applied to a microarray data consisting of patient samples
in different phases of CML identified 6 signature genes (ART4,
DDX47, IGSF2,LTB4R, SCARB1, SLC25A3).
 Results validated the gene signature using quantitative PCR: 6-
gene signature is highly predictive of CP-early vs CP-late.
 What is next?
 To identify biologically meaningful biomarkers for CML
progression and response to therapy.
 Biomarkers that are functionally related (connected in an
underlying network) to known reference genes.
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Bayesian Model Averaging
 Application 2: Survival analysis
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Bayesian Model Averaging
 Results: Breast cancer data
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Bayesian Model Averaging
 Results: Breast cancer data - Annest, Bumgarner,
Raftery, Yeung. BMC Bioinformatics 2009
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CBR
 Classification task
 Similarity measure
 Weights provided by BMA for selected features
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CBR
 Classification task
 Choose the class for which the average similar score is
highest
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CBR
 Survival analysis task
 Similarity measure
 Weights provided by BMA for selected features
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CBR
 Survival analysis task
 Choose the class for which the average similar score is
highest
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Evaluation / Classification
Dataset
Total Number
of Samples
# Training
Samples
# Validation
Samples
Number
of Genes
Leukemia 2
72
38
34
3051
Leukemia 3
72
38
34
3051
Dataset
# classes
BMA-CBR
iterativeBMA
Other
published
results
Leukemia 2
2
#genes = 20
#errors =
1/34
#genes = 20
#errors = 2/34
#genes = 5
#errors = 1/34
Leukemia 3
3
#genes = 15
#errors =
1/34
#genes = 15
#errors = 1/34
#genes ~ 40
#errors = 1/34
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Evaluation / Prediction
Dataset
Total Number
# Training
Samples
# Validation
Samples
Number
Of Genes
DLBCL
240
160
80
7,399
Breast Cancer
295
61
234
4,919
Dataset
BMA-CBR
iterativeBMA
Best Other
Published Results
DLBCL
#genes = 25
p-value = 0.00121
#genes = 25
p-value = 0.00139
#genes = 17
p-value = 0.00124
Breast cancer
#genes = 15
p-value = 2.14e-10
#genes = 15
p-value = 3.38e-10
#genes = 5
p-value = 3.12e-05
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Conclusion
 The combination of BMA and CBR provides excellent
classification and prediction results.
 It provides promising results for the application of CBR
to bioinformatics tasks and data.
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Conclusion
 Future developments
 Refine risk classes into more than two risk groups.
 Refine CBR algorithm.
 Test on additional datasets.
 Provide automatic interpretation of the classification / prediction
both for gene selection and for case-based reasoning.
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