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

Antonella Mensi, Manuele Bicego, Pietro Lovato, Marco Loog, David M.J. Tax

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)231-236
Number of pages6
JournalPattern Recognition Letters
Volume128
DOIs
Publication statusPublished - 2019

Keywords

  • Dissimilarity representation
  • Multiple-instance learning
  • Protein Remote Homology Detection

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