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.

Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning

MENSI, ANTONELLA;M. Bicego;P. Lovato;Loog, Marco;
2018-01-01

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.
2018
9783319977843
pattern recognition, bioinformatics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/992372
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