title: Multiple-input multiple-output causal strategies for gene selection.
abstract: BACKGROUND
  Traditional 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  Integrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.
authors: Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Belgium. gbonte@ulb.ac.be
authors: Gianluca
authors: G
authors: Bontempi
authors: Benjamin
authors: B
authors: Haibe-Kains
authors: Christine
authors: C
authors: Desmedt
authors: Christos
authors: C
authors: Sotiriou
authors: John
authors: J
authors: Quackenbush
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software