Benchmarking study of machine learning methods for prediction of synthetic lethality
Medea: An omics AI agent for therapeutic discovery
ELISL: Early-Late Synthetic Lethality Prediction in Cancer by Tepeli YI, Seale C, Gonçalves JP (bioRxiv 2022, Bioinformatics 2023)
NSF4SL is a negative-sample-free model for prediction of synthetic lethality (SL) based on a self-supervised contrastive learning framework.
KR4SL is a machine learning method that leverages knowledge graph reasoning to predict synthetic lethality (SL) partners for a given primary gene, capturing the structural semantics of a knowledge graph by efficiently constructing and learning from relational digraphs.
Analysis Pipeline for Synthetic Lethality Knowledge Base (SLKB)
SBSL: Selection Bias-resilient Synthetic Lethality prediction models by Seale CF, Tepeli YI, Gonçalves JP (Bioinformatics 2022)
Codebase for the synthetic lethality project -- an innovative approach for condition-specific antiviral therapeutics.
Identify synthetic lethal partners for tumour mutations.
NexLeth is a framework based on LLMs like GPT and a natural language dataset for explaining synthetic lethality (SL) mechanism.
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