ArticleBioinformatics (Oxford, England)2010
Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Article
- Computational Analysis of Virus-Host Interactomes.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- Collectively encoding protein properties enriches protein language models.BMC bioinformatics · 2022Article
- In silico prediction of HIV-1-host molecular interactions and their directionality.PLoS computational biology · 2022Article
- Host-pathogen protein-nucleic acid interactions: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions.Frontiers in cellular and infection microbiology · 2022Article
- Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota.Frontiers in cellular and infection microbiology · 2022Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- Prediction of host-pathogen protein interactions by extended network model.Turkish journal of biology = Turk biyoloji dergisi · 2021Article
- Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.Computational and structural biotechnology journal · 2020Article
- In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks.Computational and structural biotechnology journal · 2020Article
- Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks.International journal of molecular sciences · 2019Article
- Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed.Current bioinformatics · 2018Review
- Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.BMC genomics · 2018Article
- Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets.PloS one · 2018Article
- A neural network multi-task learning approach to biomedical named entity recognition.BMC bioinformatics · 2017Article
- Knowledge-transfer learning for prediction of matrix metalloprotease substrate-cleavage sites.Scientific reports · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
motivationProtein-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).
resultsWe 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.
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