ArticlePLoS computational biology2011
Network-based prediction and analysis of HIV dependency factors.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- MicroRNAs in HIV infection: dual regulators of viral replication and host immunity.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Article
- Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2.GigaScience · 2021Article
- Predicting host dependency factors of pathogens inComputational and structural biotechnology journal · 2021Article
- Article
- How HIV-1 Gag Manipulates Its Host Cell Proteins: A Focus on Interactors of the Nucleocapsid Domain.Viruses · 2020Review
- Supervised learning is an accurate method for network-based gene classification.Bioinformatics (Oxford, England) · 2020Article
- Protein-protein interactions of human viruses.Seminars in cell & developmental biology · 2020Review
- Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction.Frontiers in microbiology · 2020Review
- Network-based prediction of polygenic disease genes involved in cell motility.BMC bioinformatics · 2019Article
- A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery.BMC bioinformatics · 2019Article
- Augmenting subnetwork inference with information extracted from the scientific literature.PLoS computational biology · 2019Article
- Article
- Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets.PloS one · 2018Article
- Comparative interactomics for virus-human protein-protein interactions: DNA viruses versus RNA viruses.FEBS open bio · 2017Article
- HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings.PloS one · 2017Article
- Systems biology approach: Panacea for unravelling host-virus interactions and dynamics of vaccine induced immune response.Gene reports · 2016Review
- Review
- Global multiple protein-protein interaction network alignment by combining pairwise network alignments.BMC bioinformatics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.
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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.