ArticleBioMed research international2014
Syn-lethality: an integrative knowledge base of synthetic lethality towards discovery of selective anticancer therapies.
Article in BioMed research international, 2014. 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, 27 citations in OpenAlex.
- Cell Cycle-Based Molecular Features via Synthetic Lethality and Non-Coding RNA Interactions in Cancer.Genes · 2025Article
- Advances in Synthetic Lethality in Potential Oncology Therapeutic Approaches.Current topics in medicinal chemistry · 2025Review
- SLKB: synthetic lethality knowledge base.Nucleic acids 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
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma.Computational and structural biotechnology journal · 2022Article
- Screening and identification of genes associated with cell proliferation in cholangiocarcinoma.Aging · 2020Article
- Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization.BMC bioinformatics · 2019Article
- Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells.Molecular therapy. Nucleic acids · 2019Article
- Causal Discovery Combining K2 with Brain Storm Optimization Algorithm.Molecules (Basel, Switzerland) · 2018Article
- Bioinformatics in translational drug discovery.Bioscience reports · 2017Review
- Synthetic lethality: emerging targets and opportunities in melanoma.Pigment cell & melanoma research · 2017Review
- SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.Nucleic acids research · 2016Article
- Collateral Lethality: A new therapeutic strategy in oncology.Trends in cancer · 2015Article
- Connectivity Homology Enables Inter-Species Network Models of Synthetic Lethality.PLoS computational biology · 2015Article
- In silico prediction of synthetic lethality by meta-analysis of genetic interactions, functions, and pathways in yeast and human cancer.Cancer informatics · 2014Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic lethality (SL) is a novel strategy for anticancer therapies, whereby mutations of two genes will kill a cell but mutation of a single gene will not. Therefore, a cancer-specific mutation combined with a drug-induced mutation, if they have SL interactions, will selectively kill cancer cells. While numerous SL interactions have been identified in yeast, only a few have been known in human. There is a pressing need to systematically discover and understand SL interactions specific to human cancer. In this paper, we present Syn-Lethality, the first integrative knowledge base of SL that is dedicated to human cancer. It integrates experimentally discovered and verified human SL gene pairs into a network, associated with annotations of gene function, pathway, and molecular mechanisms. It also includes yeast SL genes from high-throughput screenings which are mapped to orthologous human genes. Such an integrative knowledge base, organized as a relational database with user interface for searching and network visualization, will greatly expedite the discovery of novel anticancer drug targets based on synthetic lethality interactions. The database can be downloaded as a stand-alone Java application.
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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.