University of Washington

Institute of Technology

Tacoma, WA, USA

Ecole des Hautes Etudes en Santé Publique

Département Infobiostat

Rennes, France

Isabelle Bichindaritz

Purpose of this TalkPurpose 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

ICCBR '10ICCBR '10

ICCBR '10ICCBR '10

ICCBR '10ICCBR '10

NCBI, 2004NCBI, 2004

Bioinformatics ChallengesBioinformatics Challenges

Frequent tasks in bioinformatics

Similarity search in genetic sequencesMicroarray data analysisMacromolecule shape predictionEvolutionary tree constructionGene regulatory network mining

ICCBR '10ICCBR '10

Bioinformatics ChallengesBioinformatics 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 specially-prepared 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.

ICCBR '10ICCBR '10

The end product of a comparative hybridization experiment is a scanned array image.

ICCBR '10ICCBR '10

Bioinformatics ChallengesBioinformatics Challenges

ICCBR '10ICCBR '10

Bioinformatics ChallengesBioinformatics Challenges

Microarray applications 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.ICCBR '10ICCBR '10

Bioinformatics ChallengesBioinformatics Challenges

ICCBR '10ICCBR '10

Data mining challenges

Volume of data (Giga bytes, number of features)

Characteristics of data (specific constraints)

Domain specific knowledge (expert interpretation)

Bioinformatics ChallengesBioinformatics Challenges

BMA-CBR SystemBMA-CBR System

ICCBR '10ICCBR '10

ICCBR '10ICCBR '10

BMA-CBR SystemBMA-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

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

ICCBR '10ICCBR '10

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.

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Feature selectionA minimal set of relevant genes for future prediction or assay

development

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Typical variable selection methods – one variable at a time

Examples:T-testBetween group to within group sum of squares (BSS/ WSS)

[Dudoit et al. 2001]

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Multivariate gene selectionOur goal: consider multiple genesSimultaneously to exploit the interdependence between genes

to reduce # relevant genes

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

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: Fewer selected genes Can be generalized to any number of classes Posterior probabilities for selected genes and selected models

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

BMAAverage 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

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

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.

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

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

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Application 1: Classification of progression of chronic myeloid leukemia (CML)

Motivation: New Candidates for Prognostic studies in CML

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Progression of CMLCML 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.

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Why predictors for CML progression?

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Identification of CML progression biomarkers

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Genes associated with CML progression

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

BMA selected genes using microarray data

Selected 6 genes over 21 modelsRepeat CV 100 times

Average Brier Score = 0.21Average prediction accuracy = 99.17%

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

PCR data: CP-early vs CP-late

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Summary: CML dataBMA 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.

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Application 2: Survival analysis

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Results: Breast cancer data

Bayesian Model AveragingBayesian Model Averaging

ICCBR '10ICCBR '10

Results: Breast cancer data - Annest, Bumgarner, Raftery, Yeung. BMC Bioinformatics 2009

Bayesian Model AveragingBayesian Model Averaging

CBRCBR

Classification taskSimilarity measure

Weights provided by BMA for selected features

ICCBR '10ICCBR '10

CBRCBR

Classification taskChoose the class for which the average similar score is

highest

ICCBR '10ICCBR '10

CBRCBR

Survival analysis taskSimilarity measure

Weights provided by BMA for selected features

ICCBR '10ICCBR '10

CBRCBR

Survival analysis taskChoose the class for which the average similar score is

highest

ICCBR '10ICCBR '10

Evaluation / ClassificationEvaluation / Classification

ICCBR '10ICCBR '10

Dataset Total Numberof Samples

# TrainingSamples

# ValidationSamples

Numberof 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

Evaluation / PredictionEvaluation / Prediction

ICCBR '10ICCBR '10

Dataset Total Number # TrainingSamples

# ValidationSamples

NumberOf Genes

DLBCL 240 160 80 7,399

Breast Cancer 295 61 234 4,919

Dataset BMA-CBR iterativeBMA Best OtherPublished Results

DLBCL #genes = 25p-value = 0.00121

#genes = 25p-value = 0.00139

#genes = 17p-value = 0.00124

Breast cancer #genes = 15p-value = 2.14e-10

#genes = 15p-value = 3.38e-10

#genes = 5p-value = 3.12e-05

ConclusionConclusion

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.

ICCBR '10ICCBR '10

ConclusionConclusion

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.

ICCBR '10ICCBR '10

ICCBR '10ICCBR '10