ArticleFrontiers in genetics2022
Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.Briefings in bioinformatics · 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
- A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo).PLoS computational biology · 2025Article
- Towards Post-Genomic Oncology: Embracing Cancer Complexity via Artificial Intelligence, Multi-Targeted Therapeutics, Drug Repurposing, and Innovative Study Designs.International journal of molecular sciences · 2025Review
- Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers.Briefings in bioinformatics · 2025Article
- MVASA-HGN: multi-view adaptive semantic-aware heterogeneous graph network forQuantitative imaging in medicine and surgery · 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
- Review
- Using graph-based model to identify cell specific synthetic lethal effects.Computational and structural biotechnology journal · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation
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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.