ArticlePLoS computational biology2019
Predicting synthetic lethal interactions using conserved patterns in protein interaction networks.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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Who cites it
22 citing papers in PubMed, 51 citations in OpenAlex.
- GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.Briefings in bioinformatics · 2026Article
- Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.Bioinformatics (Oxford, England) · 2026Article
- Dysfunction of a SET3-like complex underlies a family of related neurological disorders.Nature communications · 2026Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Graph based recurrent network for context specific synthetic lethality prediction.Science China. Life sciences · 2025Article
- 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
- Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.Briefings in bioinformatics · 2024Article
- Predicting host-based, synthetic lethal antiviral targets from omics data.NAR molecular medicine · 2024Article
- Anticancer Efficacy of KRASG12C Inhibitors Is Potentiated by PAK4 Inhibitor KPT9274 in Preclinical Models of KRASG12C-Mutant Pancreatic and Lung Cancers.Molecular cancer therapeutics · 2023Article
- Predicting host-based, synthetic lethal antiviral targets from omics data.bioRxiv : the preprint server for biology · 2023Article
- SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network.Bioinformatics (Oxford, England) · 2023Article
- Overcoming selection bias in synthetic lethality prediction.Bioinformatics (Oxford, England) · 2022Article
- Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.Bioinformatics (Oxford, England) · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Inhibitor of the Nuclear Transport Protein XPO1 Enhances the Anticancer Efficacy of KRAS G12C Inhibitors in Preclinical Models of KRAS G12C-Mutant Cancers.Cancer research communications · 2022Article
- Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.Frontiers in genetics · 2022Article
- Article
- Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality.The Journal of cell biology · 2020Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.
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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.