Protein remote homology detection using dissimilarity-based multiple instance learning

Mensi, Antonelli; Bicego, M.; Lovato, Pietro; Loog, M.; Tax, David

Protein remote homology detection using dissimilarity-based multiple instance learning

Conference paper (2018)

Authors

Antonelli Mensi University of Verona

M. Bicego University of Verona

Pietro Lovato University of Verona

M. Loog Pattern Recognition and Bioinformatics -

David Tax Pattern Recognition and Bioinformatics -

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

N-grams Multiple instance learning Protein homology

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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

A challenging Pattern Recognition problem in Bioinformatics concerns the detection of a functional relation between two proteins even when they show very low sequence similarity – this is the so-called Protein Remote Homology Detection (PRHD) problem. In this paper we propose a novel approach to PRHD, which casts the problem into a Multiple Instance Learning (MIL) framework, which seems very suitable for this context. Experiments on a standard benchmark show very competitive performances, also in comparison with alternative discriminative methods.

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