ReviewBriefings in bioinformatics2022
Computational methods, databases and tools for synthetic lethality prediction.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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.
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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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.Briefings in bioinformatics · 2024Pooled it
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- A Synthetic Lethality-Informed Multi-Omic Framework for Identifying a Five-Gene Diagnostic Signature in Chronic Obstructive Pulmonary Disease.Current issues in molecular biology · 2026Article
- Simultaneous Inhibition of ACLY and OGDH Has a Synergistic Effect on Hepatocellular Carcinoma Cell Lines.bioRxiv : the preprint server for biology · 2026Article
- Article
- Charting cell-type-specific positive genetic interaction at single-cell resolution for lung adenocarcinoma.NPJ precision oncology · 2026Article
- Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention set.Bioinformatics advances · 2026Article
- Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.Epigenomics · 2025Review
- Exploring weighting schemes for the discovery of informative generalized between pathway models to uncover pathways in genetic interaction networks.Scientific reports · 2025Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Synthetic Lethal Co-Mutations in DNA Damage Response Pathways Predict Response to Immunotherapy in Pan-Cancer.JCO precision oncology · 2025Article
- A Recent Overview of Molecular Pathways in Synthetic Lethality as a Proposed Valid Target in Oncology: Current Insights and Future Directions.Indian journal of surgical oncology · 2025Review
- Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.Briefings in bioinformatics · 2025Article
- Advances in Synthetic Lethality in Potential Oncology Therapeutic Approaches.Current topics in medicinal chemistry · 2025Review
- Benchmarking machine learning methods for synthetic lethality prediction in cancer.Nature communications · 2024Article
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives.Journal of medicinal chemistry · 2024Review
- Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer.Cancer gene therapy · 2024Article
- SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network.Bioinformatics (Oxford, England) · 2023Article
- Using graph-based model to identify cell specific synthetic lethal effects.Computational and structural biotechnology journal · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
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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.