ArticlePLoS computational biology2015
Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 35 citations in OpenAlex.
- Machine Learning Approaches for the Identification of Genetic Interactions.Methods in molecular biology (Clifton, N.J.) · 2025Review
- R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers Using Machine Learning Tools.Methods in molecular biology (Clifton, N.J.) · 2025Article
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens.Life science alliance · 2024Article
- Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens.Nature communications · 2022Article
- Overcoming selection bias in synthetic lethality prediction.Bioinformatics (Oxford, England) · 2022Article
- SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment.NPJ systems biology and applications · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Biological network topology features predict gene dependencies in cancer cell-lines.Bioinformatics advances · 2022Article
- KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers.Bioinformatics (Oxford, England) · 2021Article
- EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction.Frontiers in pharmacology · 2020Article
- Functional protein representations from biological networks enable diverse cross-species inference.Nucleic acids research · 2019Article
- Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.PLoS computational biology · 2019Article
- Review
- Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics.Journal of integrative bioinformatics · 2017Article
- Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data.Nature communications · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Synthetic lethality is a genetic interaction wherein two otherwise nonessential genes cause cellular inviability when knocked out simultaneously. Drugs can mimic genetic knock-out effects; therefore, our understanding of promiscuous drugs, polypharmacology-related adverse drug reactions, and multi-drug therapies, especially cancer combination therapy, may be informed by a deeper understanding of synthetic lethality. However, the colossal experimental burden in humans necessitates in silico methods to guide the identification of synthetic lethal pairs. Here, we present SINaTRA (Species-INdependent TRAnslation), a network-based methodology that discovers genome-wide synthetic lethality in translation between species. SINaTRA uses connectivity homology, defined as biological connectivity patterns that persist across species, to identify synthetic lethal pairs. Importantly, our approach does not rely on genetic homology or structural and functional similarity, and it significantly outperforms models utilizing these data. We validate SINaTRA by predicting synthetic lethality in S. pombe using S. cerevisiae data, then identify over one million putative human synthetic lethal pairs to guide experimental approaches. We highlight the translational applications of our algorithm for drug discovery by identifying clusters of genes significantly enriched for single- and multi-drug cancer therapies.
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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.