synthetic-lethality 开源项目推荐（14 个）

mims-harvard /Medea

Medea：一种用于治疗发现的组学人工智能代理

JieZheng-ShanghaiTech /SL_benchmark

Benchmarking study of machine learning methods for prediction of synthetic lethality

joanagoncalveslab /ELISL

ELISL: Early-Late Synthetic Lethality Prediction in Cancer by Tepeli YI, Seale C, Gonçalves JP (bioRxiv 2022, Bioinformatics 2023)

JieZheng-ShanghaiTech /KR4SL

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.

JieZheng-ShanghaiTech /NSF4SL

NSF4SL is a negative-sample-free model for prediction of synthetic lethality (SL) based on a self-supervised contrastive learning framework.

BirkanGokbag /SLKB-Analysis-Pipeline

Analysis Pipeline for Synthetic Lethality Knowledge Base (SLKB)

ccshao /mslp

Identify synthetic lethal partners for tumour mutations.

joanagoncalveslab /SBSL

SBSL: Selection Bias-resilient Synthetic Lethality prediction models by Seale CF, Tepeli YI, Gonçalves JP (Bioinformatics 2022)

Rohak72 /Synthetic-Lethality-Project

Codebase for the synthetic lethality project -- an innovative approach for condition-specific antiviral therapeutics.

mscrnt /lantern

Local-first computational cancer-research platform: reproducible dependency studies, pre-registered guardrails, adversarially reviewed reports. Hypothesis generation, not clinical claims.

ovc-bib /circ-audit

Temporal evaluation of feature-side inflation in database-driven predictors: evidence dating, vintage-matched snapshot rebuilders and rank-stability diagnostics

JieZheng-ShanghaiTech /NexLeth

NexLeth is a framework based on LLMs like GPT and a natural language dataset for explaining synthetic lethality (SL) mechanism.

ZengLLQ /flux-weave

High-Performance Flux Balance Analysis (FBA), FVA, Parsimonious FBA (pFBA) & Gene Essentiality Knockout Engine in Python.

qzhrr /AI-Driven-Therapeutic-Target-Discovery

Evidence-integrating pipeline that nominates novel, druggable small-molecule cancer targets from DepMap dependency, synthetic lethality, single-cell specificity, safety, and tractability — with an LLM nomination ensemble and a gene-masking bias control. Validated on glioblastoma.