PRESENTATION - people

ISG (Intelligent Systems Group) Researching Group

http://www.si.ehu.es/isg

Donostia- San SebastiánComputer Science Faculty - University of the Basque

Country Group leader: Pedro Larrañaga Ph.D.: Jose Lozano, Endika Bengoetxea, Iñaki Inza Ph.D. Students: Rosa Blanco, Jose L. Flores, Cristina

González, Aritz Pérez, Ramón Sagarna, Guzmán Santafé Collaborator: Jose M. Peña (Ph.D., Aalborg University),

Rubén Armañanzas

          

RESEARCH TOPICS Machine Learning – Data mining:

Learning of Bayesian networks (learning the joint probability) Bayesian networks for (supervised – unsupervised) classification Preprocess tasks: feature subset selection problem, discretization,

imputation of missing values... Optimization:

Genetic Algorithms Estimation of Distribution Algorithms (EDAs) Bayesian

networks for optimization in NP-hard problems

Applications: Medical applications (brain images, cirrhotic patients,breast

cancer, skin melanoma, etc.) Bioinformatics: classification in DNA microarrays Software testing

SEVERAL RESEARCH PROJECTS

Data mining in bioinformatics Software testing ELVIRA project:

Open source code for building-managing Bayesian networks (building, inference, propagation, abduction, classification, explanation...)

Written in Java Concurrently programmed by 5 spanish universities

http://leo.ugr.es/~elvira/

DATA MINING IN BIOINFORMATICSDNA microarrays

Genome Human Project (U.C. Santa Cruz)

http://genome.ucsc.edu

A DNA microarray sample One of the developments within Genome Project From the tissue to the scanned image Tissue microarray chip DNA mRNA hybridization

on a microarray fluorescent image scanning reflecting the expression level of thousands of genes at a time

A DNA MICROARRAY COLLECTION

Rows genes; Columns cases, samples, biopsyes, tissues, ‘cell-lines’...

SEVERAL MICROARRAY DATASETSDATASET GENES A special characteristic about

each tissue

Colon 2,000 Biopsy: ‘tumor’ vs. ‘normal’

Leukemia 7,129 Leukemia type: AML, ALL

NCI-60 1,376 9 types of tumor

Alizadeh’00 2,984 2 types of lymphoma: ‘center B-like’, ‘activates B-like’

Chen’02 17,400 Hepato celular carcinoma vs. Not liver cancer

Garber’01 >24,000 Subtypes of lung cancer

PROBLEM GOAL-TASK

The usual for biologists: Hierarchical clustering of genes Hierarchical clustering of tissues

Focusing on the specific nature of each tissue: Building of a supervised model which accurately

predicts the specific nature - characteristic of future and doubtful tissues: cancer vs. normal benignant vs. malignant tumor specific type of cancer,...

Our work: selection of relevant genes in DNA microarray SUPERVISED tasks

Small area within bioinformatics. Huge dimensionality (> 1,000) can not learn the model

at first glance selection of genes, crucial task Application goals:

Development of drugs to act over the relevant genes Therapy development Diagnostic purposes

Supervised tasks (i.e., benignant – malignant tumor) Literature: Golub et al.’99, Brazma’00, Friedman’00,

Xing & Jordan’01... For a specific disease 10-15 genes seem relevant

OUR APPROACH TO GENE SELECTION

Search algorithms: sequential (forward), EDAs... Wrapper - Filter evaluation functions Classification algorithms: naive-Bayes and

Bayesian networks, K-NN, IF-THEN rules... Made-own software and freeware software

(ELVIRA,WEKA, MLC++...) Our ‘Talón de Aquiles’ (weak point):

Biological interpretation of induced models and selected genes, validity of obtained recognition accuracy...

PUBLICATIONS IN BIOINFORMATICS

R. Blanco, P. Larrañaga, I. Inza, B. Sierra (2004). “Gene selection for cancer classification using wrapper approaches”. International Journal of Pattern Recognition and Artificial Intelligence

I. Inza, P. Larrañaga, R. Blanco, A. J. Cerrolaza (2003). “Filter versus wrapper gene selection approaches in DNA microarray domains”. Artificial Intelligence in Medicine Journal. Special issue in “Data mining in Genomics and Proteomics”

I. Inza, B. Sierra, R. Blanco, P. Larrañaga (2002). “Gene selection by sequential search wrapper approaches in microarray cancer class prediction”. Journal of Intelligent and Fuzzy Systems. Special issue in Bioinformatics

INTERESTING REFERENCES Conferences:

ISMB: International Symposium on Molecular Biology ECCB: European Conference on Computational Biology CAMDA: Critical Assesment of Microarray Data Analysis WABI: Workshop on Algorithms in Bioinformatics

Reference journal: “Bioinformatics” and special issues of machine learning journals on the topic

Web sites: Stanford Genomic Resources Stanford Microarray Database http://www.gene-chips.com/ Hebrew University (N. Friedman, D. Pe’er, I. Nachman...) Tel Aviv University (R. Shamir) Human Genome Working Draft: http://genome.ucsc.edu

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