ArticleNucleic acids research2016
SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.
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Who cites it
77 citing papers in PubMed, 1 synthesis or guideline pooled it, 161 citations in OpenAlex.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions.Briefings in bioinformatics · 2026Review
- Synthetic rescue in Saccharomyces cerevisiae: Concepts, large-scale genetic mapping, and functional implications.Journal of microbiology (Seoul, Korea) · 2026Review
- Knowledge-based citation reasoning for biomedical domain.Bioinformatics (Oxford, England) · 2026Article
- Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction.BMC bioinformatics · 2025Article
- Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.Epigenomics · 2025Review
- A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo).PLoS computational biology · 2025Article
- The synthetic lethal interaction between CDS1 and CDS2 is a vulnerability in uveal melanoma and across multiple tumor types.Nature genetics · 2025Article
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Cell Cycle-Based Molecular Features via Synthetic Lethality and Non-Coding RNA Interactions in Cancer.Genes · 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
- Advances in Synthetic Lethality in Potential Oncology Therapeutic Approaches.Current topics in medicinal chemistry · 2025Review
- An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.Journal of advanced research · 2024Article
- Global research and current trends on nanotherapy in lung cancer research: a bibliometric analysis of 20 years.Discover oncology · 2024Article
- Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning.Bioinformatics advances · 2024Article
- Engineering Self-Assembled Nanomedicines Composed of Clinically Approved Medicines for Enhanced Tumor Nanotherapy.Nanomaterials (Basel, Switzerland) · 2023Article
17 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.
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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.