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2018 | OriginalPaper | Buchkapitel

Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning

verfasst von : Antonelli Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M. J. Tax

Erschienen in: Structural, Syntactic, and Statistical Pattern Recognition

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Learning Deep Embeddings via Margin-Based Discriminate Loss

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Titel: Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning
verfasst von: Antonelli Mensi
Manuele Bicego
Pietro Lovato
Marco Loog
David M. J. Tax
Verlag: Springer International Publishing
Buch: Structural, Syntactic, and Statistical Pattern Recognition
Print ISBN: 978-3-319-97784-3

Electronic ISBN: 978-3-319-97785-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-97785-0_12

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