Supervised selective kernel fusion for membrane protein prediction

Tatarchuk, Alexander, Sulimova, Valentina, Torshin, Ivan, Mottl, Vadim and Windridge, David ORCID: (2014) Supervised selective kernel fusion for membrane protein prediction. Pattern Recognition in Bioinformatics: 9th IAPR International Conference, PRIB 2014, Stockholm, Sweden, August 21-23, 2014. Proceedings. In: 9th IAPR International Conference Pattern Recognition in Bioinformatics (PRIB 2014), 21-23 Aug 2014, Stockholm, Sweden. ISBN 9783319091914. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-319-09192-1_9)

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Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published as a Chapter in: Pattern Recognition in Bioinformatics, Volume 8626 of the series Lecture Notes in Computer Science, pp 98-109
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 19500
Notes on copyright: The final publication is available at Springer via
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Depositing User: David Windridge
Date Deposited: 22 Apr 2016 11:02
Last Modified: 11 Feb 2021 05:47

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