TY - JOUR
T1 - A dissimilarity-based multiple instance learning approach for protein remote homology detection
AU - Mensi, Antonella
AU - Bicego, Manuele
AU - Lovato, Pietro
AU - Loog, Marco
AU - Tax, David M.J.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Dissimilarity representation
KW - Multiple-instance learning
KW - Protein Remote Homology Detection
UR - http://www.scopus.com/inward/record.url?scp=85072200336&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2019.08.027
DO - 10.1016/j.patrec.2019.08.027
M3 - Article
AN - SCOPUS:85072200336
SN - 0167-8655
VL - 128
SP - 231
EP - 236
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
ER -