ArticleNature communications2013
Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets.
Article in Nature communications, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 49 citations in OpenAlex.
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Neuropsychopharmacology in the era of artificial intelligence and biomolecule prediction software.NPP - digital psychiatry and neuroscience · 2025Review
- SUMOylation-Driven mRNA Circularization Enhances Translation and Promotes Lymphatic Metastasis of Bladder Cancer.Cancer research · 2024Article
- SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis.Database : the journal of biological databases and curation · 2022Article
- Inference of mutability landscapes of tumors from single cell sequencing data.PLoS computational biology · 2020Article
- Beyond Synthetic Lethality: Charting the Landscape of Pairwise Gene Expression States Associated with Survival in Cancer.Cell reports · 2019Article
- Harnessing synthetic lethality to predict the response to cancer treatment.Nature communications · 2018Article
- Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence.Cell reports · 2017Article
- Precision Oncology: The Road Ahead.Trends in molecular medicine · 2017Review
- Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics.Journal of integrative bioinformatics · 2017Article
- Epistasis in genomic and survival data of cancer patients.PLoS computational biology · 2017Article
- Adaptive evolution of complex innovations through stepwise metabolic niche expansion.Nature communications · 2016Article
- Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes.Oncogene · 2016Review
- SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.Nucleic acids research · 2016Article
- Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Prediction of Genetic Interactions Using Machine Learning and Network Properties.Frontiers in bioengineering and biotechnology · 2015Review
- Article
- A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening.Genome medicine · 2014Article
- High-resolution network biology: connecting sequence with function.Nature reviews. Genetics · 2013Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets.
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