---
title: "AI4Bio landscape enrichment overlay"
task: ""
lineage_type: import
upstream_source: https://github.com/inoue0426/awesome-computational-biology/blob/7a064bf0/data/enrichment.yml
upstream_sha: 7a064bf0
imported_at: 2026-08-08
prompt_class: catalogue
upstream_changes: accepted
author: upstream
validated: false
---

# AI4Bio landscape enrichment overlay
#
# README.md remains the canonical source for resource membership and basic fields.
# Add richer, independently curated metadata here, keyed by the stable resource id.
# scripts/build_resources.py merges these fields into generated JSON/CSV artifacts.
#
# Enrichment values should be source-verifiable. Controlled vocabulary fields are
# validated against data/vocabulary.yml.

resources:
  scgpt:
    entities: [cell, gene]
    methods: [generative-model, self-supervised-learning, transformer]
    modalities: [multi-omics, single-cell-rna-seq, transcriptomics]
    tasks:
      - cell-type-annotation
      - foundation-model-pretraining
      - gene-regulatory-network-inference
      - perturbation-prediction
      - representation-learning
    year: 2024
    github: https://github.com/bowang-lab/scGPT
    documentation: https://scgpt.readthedocs.io/en/latest/
    paper: https://www.nature.com/articles/s41592-024-02201-0
    last_checked: 2026-08-08
    metadata_sources:
      - https://github.com/bowang-lab/scGPT
      - https://www.nature.com/articles/s41592-024-02201-0

  geneformer:
    entities: [cell, gene]
    methods: [self-supervised-learning, transformer]
    modalities: [single-cell-rna-seq, transcriptomics]
    tasks:
      - classification
      - foundation-model-pretraining
      - perturbation-prediction
      - representation-learning
    year: 2023
    documentation: https://geneformer.readthedocs.io/
    paper: https://www.nature.com/articles/s41586-023-06139-9
    last_checked: 2026-08-08
    metadata_sources:
      - https://huggingface.co/ctheodoris/Geneformer
      - https://www.nature.com/articles/s41586-023-06139-9

  scfoundation:
    entities: [cell, gene]
    methods: [self-supervised-learning, transformer]
    modalities: [single-cell-rna-seq, transcriptomics]
    tasks:
      - cell-type-annotation
      - drug-response-prediction
      - foundation-model-pretraining
      - perturbation-prediction
      - representation-learning
    year: 2024
    github: https://github.com/biomap-research/scFoundation
    paper: https://www.nature.com/articles/s41592-024-02305-7
    last_checked: 2026-08-08
    metadata_sources:
      - https://github.com/biomap-research/scFoundation
      - https://www.nature.com/articles/s41592-024-02305-7

  genecompass:
    entities: [cell, gene]
    methods: [self-supervised-learning, transformer]
    modalities: [single-cell-rna-seq, transcriptomics]
    tasks: [foundation-model-pretraining, representation-learning]
    year: 2024
    github: https://github.com/xCompass-AI/GeneCompass
    paper: https://www.nature.com/articles/s41422-024-01034-y
    last_checked: 2026-08-08
    metadata_sources:
      - https://github.com/xCompass-AI/GeneCompass
      - https://www.nature.com/articles/s41422-024-01034-y

  uce:
    entities: [cell]
    methods: [self-supervised-learning]
    modalities: [single-cell-rna-seq, transcriptomics]
    tasks: [foundation-model-pretraining, representation-learning]
    year: 2026
    github: https://github.com/snap-stanford/UCE
    paper: https://www.nature.com/articles/s41586-026-10689-z
    last_checked: 2026-08-08
    metadata_sources:
      - https://github.com/snap-stanford/UCE
      - https://www.nature.com/articles/s41586-026-10689-z

  cellplm:
    entities: [cell, gene]
    methods: [self-supervised-learning, transformer]
    modalities: [single-cell-rna-seq, transcriptomics]
    tasks: [foundation-model-pretraining, representation-learning]
    year: 2023
    github: https://github.com/OmicsML/CellPLM
    paper: https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1
    last_checked: 2026-08-08
    metadata_sources:
      - https://github.com/OmicsML/CellPLM
      - https://www.biorxiv.org/content/10.1101/2023.10.03.560734v1

  scbert:
    entities: [cell, gene]
    methods: [language-model, self-supervised-learning, transformer]
    modalities: [single-cell-rna-seq, transcriptomics]
    tasks: [cell-type-annotation, classification, foundation-model-pretraining]
    year: 2022
    github: https://github.com/TencentAILabHealthcare/scBERT
    paper: https://www.nature.com/articles/s42256-022-00534-z
    last_checked: 2026-08-08
    metadata_sources:
      - https://github.com/TencentAILabHealthcare/scBERT
      - https://www.nature.com/articles/s42256-022-00534-z