ArticleFrontiers in pharmacology2020
EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction.
Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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
- Graph based recurrent network for context specific synthetic lethality prediction.Science China. Life sciences · 2025Article
- R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers Using Machine Learning Tools.Methods in molecular biology (Clifton, N.J.) · 2025Article
- An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.Journal of advanced research · 2024Article
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- ELISL: early-late integrated synthetic lethality prediction in cancer.Bioinformatics (Oxford, England) · 2024Article
- Text-mining-based feature selection for anticancer drug response prediction.Bioinformatics advances · 2024Article
- 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
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers.Bioinformatics (Oxford, England) · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.
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