TY  - JOUR
AU  - Qi, Y.
AU  - Tastan, O.
AU  - Carbonell, J.G.
AU  - Klein-Seetharaman, J.
AU  - Weston, J.
TI  - Semi-Supervised Multi-Task Learning for Predicting Interactions between HIV-1 and Human Proteins
JO  - Bioinformatics
VL  - 26
SN  - 1367-4803
CY  - Oxford
PB  - Oxford University Press
M1  - PreJuSER-13095
SP  - 1645 - 1652
PY  - 2010
N1  - Record converted from VDB: 12.11.2012
AB  - Protein-protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction (partially labeled).We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information.http://www.cs.cmu.edu/~qyj/HIVsemi.
KW  - Algorithms
KW  - Artificial Intelligence
KW  - Computational Biology: methods
KW  - Data Interpretation, Statistical
KW  - HIV-1: physiology
KW  - Human Immunodeficiency Virus Proteins: metabolism
KW  - Humans
KW  - Models, Statistical
KW  - Protein Interaction Mapping: methods
KW  - Proteins: metabolism
KW  - Human Immunodeficiency Virus Proteins (NLM Chemicals)
KW  - Proteins (NLM Chemicals)
KW  - J (WoSType)
LB  - PUB:(DE-HGF)16
C6  - pmid:20823334
C2  - pmc:PMC2935441
UR  - <Go to ISI:>//WOS:000281714100035
DO  - DOI:10.1093/bioinformatics/btq394
UR  - https://juser.fz-juelich.de/record/13095
ER  -