ArticleInfection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2011
Supervised learning and prediction of physical interactions between human and HIV proteins.
Article in Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 97 citations in OpenAlex.
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- Multilabel prediction of virus target proteins via multimodal graph representation learning.PLoS computational biology · 2026Article
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- Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Combinatorial interactions between viral proteins expand the potential functional landscape of the tomato yellow leaf curl virus proteome.PLoS pathogens · 2022Article
- Computational modeling of human-nCoV protein-protein interaction network.Methods (San Diego, Calif.) · 2022Article
- A virus-target host proteins recognition method based on integrated complexes data and seed extension.BMC bioinformatics · 2022Article
- In silico prediction of HIV-1-host molecular interactions and their directionality.PLoS computational biology · 2022Article
- 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
- Bioinformatics-Based Approaches to Study Virus-Host Interactions During SARS-CoV-2 Infection.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions.Frontiers in microbiology · 2022Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- Article
- Prediction of host-pathogen protein interactions by extended network model.Turkish journal of biology = Turk biyoloji dergisi · 2021Article
- Protein-protein interactions of human viruses.Seminars in cell & developmental biology · 2020Review
- SARS-CoV-2-human protein-protein interaction network.Informatics in medicine unlocked · 2020Article
- Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.Computational and structural biotechnology journal · 2020Article
- Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions.Briefings in functional genomics · 2018Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundInfectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions. METHODOLOGY: We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human-HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell. SIGNIFICANCE: Unlike intra-species PPIs, host-pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host-pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.