Multiple-input multiple-output causal strategies for gene selection

BMC Bioinformatics

Research Article

Christos Sotiriou¹  Christine Desmedt¹  Benjamin Haibe-Kains²  John Quackenbush²  Gianluca Bontempi³ 

[1] Breast Cancer Translational Research Laboratory, Department of Medical Oncology, Institut Jules Bordet, Université Libre de Bruxelles, Belgium;Computational Biology and Functional Genomics Laboratory, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, USA;Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium;

关键词: Gene Ontological;    Feature Selection;    Mutual Information;    Causal Dependency;    Causal Information;   

DOI  :  10.1186/1471-2105-12-458

 received in 2011-05-27, accepted in 2011-11-25,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundTraditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting.ResultsWe show in synthetic case study that a better prioritization of causal variables can be obtained by considering a relevance score which incorporates a causal term. In addition we show, in a meta-analysis study of six publicly available breast cancer microarray datasets, that the improvement occurs also in terms of accuracy. The biological interpretation of the results confirms the potential of a causal approach to gene selection.ConclusionsIntegrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.

【 授权许可】

CC BY   
© Bontempi et al; licensee BioMed Central Ltd. 2011