An assessment of feature relevance in predicting protein function from sequence

Al-Shahib, A., He, C., Tan, A.C., Girolami, M. and Gilbert, D.G. (2004) An assessment of feature relevance in predicting protein function from sequence. Lecture Notes in Computer Science, 3177, pp. 52-57. (doi:10.1007/b99975)



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Improving the performance of protein function prediction is the ultimate goal for a bioinformatician working in functional genomics. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived features using machine learning. In this case the choice of possible features which can be derived from the sequence is of vital importance to ensure adequate discrimination to predict function. In this paper we have shown that carefully assessing the discriminative value of derived features by performing feature selection improves the performance of the prediction classifiers by eliminating irrelevant and redundant features. The subset selected from available features has also shown to be biologically meaningful as they correspond to features that have commonly been employed to assess biological function.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Gilbert, Prof David and Girolami, Prof Mark
Authors: Al-Shahib, A., He, C., Tan, A.C., Girolami, M., and Gilbert, D.G.
Subjects:Q Science > QH Natural history > QH301 Biology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Copyright Holders:Copyright © 2004 Springer
First Published:First published in Lecture Notes in Computer Science 3177:52-57
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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