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. Paper abstract bibtex 1 download 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}
}
Downloads: 1
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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","bibtex":"@InProceedings{\t puelma:etal:2009,\n author\t= {T. Puelma and A. Soto and R. Gutierrez},\n title\t\t= {An ensemble of Discriminative Local Subspaces in\n\t\t Microarray Data for Gene Ontology Annotation Predictions},\n booktitle\t= {Proc. of 1st Chilean Workshop on Pattern Recognition\n\t\t (CWPR)},\n pages\t\t= {52-61},\n year\t\t= {2009},\n abstract\t= {Genome sequencing has allowed to know almost every gene of\n\t\t many organisms. However, understanding the functions of\n\t\t most genes is still an open problem. In this paper, we\n\t\t present a novel machine learning method to predict\n\t\t functions of unknown genes in base of gene expression data\n\t\t and Gene Ontology annotations. Most function prediction al-\n\t\t gorithms developed in the past don’t exploit the\n\t\t discriminative power of supervised learning. 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