@inproceedings{a3d844d728ce47d78fd21888a15248d2,
title = "Protein remote homology detection using dissimilarity-based multiple instance learning",
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.",
keywords = "Multiple instance learning, N-grams, Protein homology",
author = "Antonelli Mensi and Manuele Bicego and Pietro Lovato and Marco Loog and Tax, {David M.J.}",
year = "2018",
doi = "10.1007/978-3-319-97785-0_12",
language = "English",
isbn = "978-3-319-97784-3",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "119--129",
editor = "X. Bai and Hancock, {E.R. } and T.K. Ho and Wilson, {R.C. } and Biggio, {B. } and Robles-Kelly, {A. }",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition",
note = "Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018 ; Conference date: 17-08-2018 Through 19-08-2018",
}