An ensemble of Discriminative Local Subspaces in Microarray Data for Gene Ontology Annotation Predictions

An ensemble of Discriminative Local Subspaces in Microarray Data for Gene Ontology Annotation Predictions. Puelma, T., Soto, A., & Gutierrez, R. In Proc. of 1st Chilean Workshop on Pattern Recognition (CWPR), pages 52-61, 2009.

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Genome sequencing has allowed to know almost every gene of many organisms. However, understanding the functions of most genes is still an open problem. In this paper, we present a novel machine learning method to predict functions of unknown genes in base of gene expression data and Gene Ontology annotations. Most function prediction al- gorithms developed in the past don’t exploit the discriminative power of supervised learning. In contrast, our method uses this to find discriminative local subspaces that are suitable to perform gene functional prediction. Cross-validation test are done in artificial and real data and compared with a state-of- the-art method. Preliminary results shows that in overall, our method outperforms the other approach in terms of precision and recall, giving insights in the importance of a good selection of discriminative experiments.

@InProceedings{	  puelma:etal:2009,
  author	= {T. Puelma and A. Soto and R. Gutierrez},
  title		= {An ensemble of Discriminative Local Subspaces in
		  Microarray Data for Gene Ontology Annotation Predictions},
  booktitle	= {Proc. of 1st Chilean Workshop on Pattern Recognition
		  (CWPR)},
  pages		= {52-61},
  year		= {2009},
  abstract	= {Genome sequencing has allowed to know almost every gene of
		  many organisms. However, understanding the functions of
		  most genes is still an open problem. In this paper, we
		  present a novel machine learning method to predict
		  functions of unknown genes in base of gene expression data
		  and Gene Ontology annotations. Most function prediction al-
		  gorithms developed in the past don’t exploit the
		  discriminative power of supervised learning. In contrast,
		  our method uses this to find discriminative local subspaces
		  that are suitable to perform gene functional prediction.
		  Cross-validation test are done in artificial and real data
		  and compared with a state-of- the-art method. Preliminary
		  results shows that in overall, our method outperforms the
		  other approach in terms of precision and recall, giving
		  insights in the importance of a good selection of
		  discriminative experiments.},
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/DLS-Final-v2.pdf}
}

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