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• W2053707267 abstract "Supervised learning methods have been recently exploited to learn gene regulatory networks from gene expression data. They consist of building a binary classifier from feature vectors composed by expression levels of a set of known regulatory connections, available in public databases (eg. RegulonDB, TRRD, Transfac, IPA), and using such a classifier to predict new unknown connections. The input to a binary supervised classifier consists normally of positive and negative examples, but usually the only available information are a partial set of gene regulations, i.e. positive examples, and unlabeled data which could include both positive and negative examples. A fundamental challenge is the choice of negative examples from such unlabeled data to make the classifier able to learn from data. We exploit the known topology of a gene network to select such negative examples and show whether such an assumption benefits the performance of a classifier." @default.
• W2053707267 created "2016-06-24" @default.
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• W2053707267 date "2009-11-01" @default.
• W2053707267 modified "2023-09-26" @default.
• W2053707267 title "Selection of negative examples in learning gene regulatory networks" @default.
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• W2053707267 doi "https://doi.org/10.1109/bibmw.2009.5332137" @default.
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