ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2009
Prediction of interactions between HIV-1 and human proteins by information integration.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
53 citing papers in PubMed, 139 citations in OpenAlex.
- Multilabel prediction of virus target proteins via multimodal graph representation learning.PLoS computational biology · 2026Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 2024Article
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.PloS one · 2023Article
- PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.Database : the journal of biological databases and curation · 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
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- Graph embeddings on gene ontology annotations for protein-protein interaction prediction.BMC bioinformatics · 2020Article
- Protein-protein interactions of human viruses.Seminars in cell & developmental biology · 2020Review
- Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction.Frontiers in genetics · 2020Article
- Prediction of human-Bacillus anthracis protein-protein interactions using multi-layer neural network.Bioinformatics (Oxford, England) · 2018Article
- Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed.Current bioinformatics · 2018Review
- Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets.PloS one · 2018Article
- A review on host-pathogen interactions: classification and prediction.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2016Review
- Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways.Scientific reports · 2016Article
- A NMF based approach for integrating multiple data sources to predict HIV-1-human PPIs.BMC bioinformatics · 2016Article
- Classifying pairs with trees for supervised biological network inference.Molecular bioSystems · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Human immunodeficiency virus-1 (HIV-1) in acquired immune deficiency syndrome (AIDS) relies on human host cell proteins in virtually every aspect of its life cycle. Knowledge of the set of interacting human and viral proteins would greatly contribute to our understanding of the mechanisms of infection and subsequently to the design of new therapeutic approaches. This work is the first attempt to predict the global set of interactions between HIV-1 and human host cellular proteins. We propose a supervised learning framework, where multiple information data sources are utilized, including co-occurrence of functional motifs and their interaction domains and protein classes, gene ontology annotations, posttranslational modifications, tissue distributions and gene expression profiles, topological properties of the human protein in the interaction network and the similarity of HIV-1 proteins to human proteins' known binding partners. We trained and tested a Random Forest (RF) classifier with this extensive feature set. The model's predictions achieved an average Mean Average Precision (MAP) score of 23%. Among the predicted interactions was for example the pair, HIV-1 protein tat and human vitamin D receptor. This interaction had recently been independently validated experimentally. The rank-ordered lists of predicted interacting pairs are a rich source for generating biological hypotheses. Amongst the novel predictions, transcription regulator activity, immune system process and macromolecular complex were the top most significant molecular function, process and cellular compartments, respectively. Supplementary material is available at URL www.cs.cmu.edu/õznur/hiv/hivPPI.html
Indexed as
Identifiers
19209727PMC3263379W2077398039What OpenQuestion holds
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.