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  Feature selection and transduction for prediction of molecular bioactivity for drug design

Weston, J., Perez-Cruz, F., Bousquet, O., Chapelle, O., Elisseeff, A., & Schölkopf, B. (2003). Feature selection and transduction for prediction of molecular bioactivity for drug design. Bioinformatics, 19(6), 764-771. doi:10.1093/bioinformatics/btg054.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DCA5-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6AB4-9
Genre: Journal Article

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 Creators:
Weston, J1, 2, Author              
Perez-Cruz, F, Author
Bousquet, O1, 2, Author              
Chapelle, O1, 2, Author              
Elisseeff, A1, 2, Author              
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Motivation: In drug discovery a key task is to identify characteristics that separate active (binding) compounds from inactive (non-binding) ones. An automated prediction system can help reduce resources necessary to carry out this task. Results: Two methods for prediction of molecular bioactivity for drug design are introduced and shown to perform well in a data set previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001. The data is characterized by very few positive examples, a very large number of features (describing three-dimensional properties of the molecules) and rather different distributions between training and test data. Two techniques are introduced specifically to tackle these problems: a feature selection method for unbalanced data and a classifier which adapts to the distribution of the the unlabeled test data (a so-called transductive method). We show both techniques improve identification performance and in conjunction provide an improvement over using only one of the techniques. Our results suggest the importance of taking into account the characteristics in this data which may also be relevant in other problems of a similar type.

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 Dates: 2003-04
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 2823
DOI: 10.1093/bioinformatics/btg054
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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 19 (6) Sequence Number: - Start / End Page: 764 - 771 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991