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.

Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition
Subtitle of host publicationJoint IAPR International Workshop, S+SSPR 2018, Proceedings
EditorsX. Bai, E.R. Hancock, T.K. Ho, R.C. Wilson, B. Biggio, A. Robles-Kelly
Place of PublicationCham
PublisherSpringer Verlag
Pages119-129
Number of pages11
ISBN (Electronic)978-3-319-97785-0
ISBN (Print)978-3-319-97784-3
DOIs
Publication statusPublished - 2018
EventJoint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018 - Beijing, China
Duration: 17 Aug 201819 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume11004
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceJoint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018
CountryChina
CityBeijing
Period17/08/1819/08/18

    Research areas

  • Multiple instance learning, N-grams, Protein homology

ID: 47578862