ArticleBMC bioinformatics2019
Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.Briefings in bioinformatics · 2026Article
- Uncertainty-aware synthetic lethality prediction with pretrained foundation models.bioRxiv : the preprint server for biology · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 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
- Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.Briefings in bioinformatics · 2025Article
- Benchmarking machine learning methods for synthetic lethality prediction in cancer.Nature communications · 2024Article
- Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.Briefings in bioinformatics · 2024Article
- ELISL: early-late integrated synthetic lethality prediction in cancer.Bioinformatics (Oxford, England) · 2024Article
- MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.Frontiers in pharmacology · 2024Article
- Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients.Scientific reports · 2023Article
- 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
- Overcoming selection bias in synthetic lethality prediction.Bioinformatics (Oxford, England) · 2022Article
- SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.Database : the journal of biological databases and curation · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.Frontiers in genetics · 2022Article
- KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSynthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.
resultsIn this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.
conclusionsIn this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.
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