Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins

By Yanjun Qi, Oznur Tastan, Jaime G. Carbonell, Judith Klein-Seetharaman, Jason Aaron Edward Weston. 2010

157 citations recorded by the source corpus.

Motivation: 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). Results: 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. Availability: http://www.cs.cmu.edu/∼qyj/HIVsemi Contact: qyj@cs.cmu.edu

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

A Survey on Multi-Task Learning (2017, methodology): “Feature Transformation Visual tracking [192], [193] Protein subcellular location prediction [194] Speech synthesis [195], [196] Learning to rank [197] Stock prediction [198] Action recognition [199] Protein interaction prediction [200] Speech recognition [201], [202] Localization [203] Facial landma”
Multitask Matrix Completion for Learning Protein Interactions Across Diseases (2016, background): “Qi et al. (2010) proposed a semisupervised multitask framework to predict PPIs from partially labeled reference sets.”
Multitask learning for host–pathogen protein interactions (2013, unknown): “Qi et al.”