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SL_benchmark

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therapeutics/sl-benchmark

Benchmarking study of machine learning methods for prediction of synthetic lethality

Machine-generated from the listed sources and not yet reviewed by a human.

SL_benchmark project image
GitHub preview card for JieZheng-ShanghaiTech/SL_benchmark. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
ai4science · cancer-genomics · data-science · database · deep-learning · drug-discovery · drug-targets · genetic-interactions · graph-neural-networks · knowledge-graph · machine-learning · synthetic-lethality
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • NSF4SLdrug-discovery · synthetic-lethality

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

  • Progenidrug-discovery · graph-neural-networks · knowledge-graph

    "A Probabilistic Knowledge Graph for Target Identification"

  • TxGNNdrug-discovery · graph-neural-networks · knowledge-graph

    TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design

  • pymol-map-pocketdrug-discovery · drug-targets

    PyMOL extension to map a protein pocket, generated from PockDrug, to the full protein structure.

  • pharmOncoXdrug-targets

    Targeted and non-targeted anticancer drugs and drug regimens

  • ChemFlowai4science · drug-discovery

    Uncover meaningful structures of latent spaces learned by generative models with flows!

sources
  1. api.github.com/repos/JieZheng-ShanghaiTech/SL_benchmark
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2024-11-15, 22 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/44.json→ .entries["sl-benchmark"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.