ArticleBioinformatics (Oxford, England)2021
KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.Briefings in bioinformatics · 2026Article
- Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions.Briefings in bioinformatics · 2026Review
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning.Scientific reports · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Uncertainty-aware synthetic lethality prediction with pretrained foundation models.bioRxiv : the preprint server for biology · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Context-aware gene dependency modeling via graph attention networks for precision oncology.Journal of translational medicine · 2025Article
- A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo).PLoS computational biology · 2025Article
- Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.Epigenomics · 2025Review
- Article
- A Survey on Unifying Large Language Models and Knowledge Graphs for Biomedicine and Healthcare.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.Briefings in bioinformatics · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Machine Learning Approaches for the Identification of Genetic Interactions.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Struct2SL: Synthetic lethality prediction based on AlphaFold2 structure information and Multilayer Perceptron.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationSynthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge.
resultsHere, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction. AVAILABILITY AND IMPLEMENTATION: : KG4SL is freely available at https://github.com/JieZheng-ShanghaiTech/KG4SL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.