A dissimilarity-based multiple instance learning approach for protein remote homology detection

Journal article (2019)

Authors

Antonella Mensi University of Verona

Manuele Bicego University of Verona

Pietro Lovato University of Verona

M. Loog Pattern Recognition and Bioinformatics -

David M. J. Tax Pattern Recognition and Bioinformatics -

David M.J. Tax Pattern Recognition and Bioinformatics -

D.M.J. Tax Pattern Recognition and Bioinformatics -

David Tax Pattern Recognition and Bioinformatics -

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Multiple-instance learning Dissimilarity representation Protein Remote Homology Detection

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http://resolver.tudelft.nl/uuid:3a5a6222-6560-48e9-b860-053a671b7c84

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Published Date

2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

We study the problem of Protein Remote Homology Detection, which assesses the functional similarity of two proteins. We approach this as a problem of binary multiple-instance learning (MIL) that aims to distinguish between homologous and non-homologous proteins. The particular MIL approach employed is based on the dissimilarity representation in which various schemes of combining N-gram representations are considered. This approach allows us to cope with longer N-grams, capturing a richer biological context, and results in versatile framework offering competitive performance compared to state of the art.

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