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Rename expression categories to Bulk expression and Single-cell expression
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[
{
"algorithm": "AAC.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 93.91,
"dataset_path": "data/embeddings/probe_aac",
"description": "Protein representation based on amino acid composition, using the relative frequencies of the 20 standard amino acids in each protein sequence.",
"id": "probe_aac",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_aac/metadata.json",
"modality": "Protein sequence",
"n_dims": 20,
"n_genes": 18873,
"name": "AAC",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_aac",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "ALBERT.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 91.82,
"dataset_path": "data/embeddings/probe_albert",
"description": "Protein sequence representation produced by the ALBERT protein language model.",
"id": "probe_albert",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_albert/metadata.json",
"modality": "Protein sequence",
"n_dims": 4096,
"n_genes": 18453,
"name": "ALBERT",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_albert",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "Google DeepMind AlphaGenome API regulatory predictions. signed log1p per summary feature, z-score across genes, PCA to 256 dimensions.",
"category": "Regulatory genomics",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 99.94,
"dataset_path": "data/embeddings/alphagenome_tss1mb_regulatory_tracksummary_pca256",
"description": "Gene-level regulatory representation derived from AlphaGenome predictions across multiple regulatory tracks around each transcription start site, summarized and reduced to 256 dimensions.",
"id": "alphagenome_tss1mb_regulatory_tracksummary_pca256",
"license": "Original source / license terms",
"license_url": "https://github.com/google-deepmind/alphagenome",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/alphagenome_tss1mb_regulatory_tracksummary_pca256/metadata.json",
"modality": "Regulatory genomics",
"n_dims": 256,
"n_genes": 20084,
"name": "AlphaGenome",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/alphagenome_tss1mb_regulatory_tracksummary_pca256",
"qc_status": "processed_qc_passed",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "APAAC.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 93.9,
"dataset_path": "data/embeddings/probe_apaac",
"description": "Protein representation based on amphiphilic pseudo-amino-acid composition, combining amino-acid composition with sequence-order correlation features.",
"id": "probe_apaac",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_apaac/metadata.json",
"modality": "Protein sequence",
"n_dims": 80,
"n_genes": 18871,
"name": "APAAC",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_apaac",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "Basenji2 sequence-to-function model (checkpoint: Basenji2 PyTorch port). mean of central 3 output bins around TSS. log1p raw human central3 mean tracks, then dimension-wise z-score across genes.",
"category": "Regulatory genomics",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 100.0,
"dataset_path": "data/embeddings/basenji2_tss_131kb_human_central3",
"description": "Gene-level regulatory representation from Basenji2 predictions on 131-kb DNA windows centered at each transcription start site, summarized across the central output bins.",
"id": "basenji2_tss_131kb_human_central3",
"license": "Original source / license terms",
"license_url": "https://zenodo.org/records/14969593",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/basenji2_tss_131kb_human_central3/metadata.json",
"modality": "Regulatory genomics",
"n_dims": 5313,
"n_genes": 20097,
"name": "Basenji2",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/basenji2_tss_131kb_human_central3",
"qc_status": "processed_qc_passed",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BERT-BFD.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 91.82,
"dataset_path": "data/embeddings/probe_bert_bfd",
"description": "Protein sequence representation produced by the ProtTrans BERT-BFD model.",
"id": "probe_bert_bfd",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_bert_bfd/metadata.json",
"modality": "Protein sequence",
"n_dims": 1024,
"n_genes": 18453,
"name": "BERT-BFD",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_bert_bfd",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BERT-PFAM.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 91.86,
"dataset_path": "data/embeddings/probe_bert_pfam",
"description": "Protein sequence representation produced by a BERT model trained on Pfam protein-domain sequences.",
"id": "probe_bert_pfam",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_bert_pfam/metadata.json",
"modality": "Protein sequence",
"n_dims": 768,
"n_genes": 18461,
"name": "BERT-PFAM",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_bert_pfam",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BioBERT-GeneProteinSummary (checkpoint: dmis-lab/biobert-base-cased-v1.2). final hidden layer mean pooling over valid non-special, non-padding tokens.",
"category": "Biomedical text",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 95.38,
"dataset_path": "data/embeddings/biobert_gene_protein_summary",
"description": "Biomedical-text representation obtained by encoding each gene's NCBI summary plus its UniProt protein summary, when available, with BioBERT and mean-pooling the token representations.",
"id": "biobert_gene_protein_summary",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/dmis-lab/biobert-base-cased-v1.2",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/biobert_gene_protein_summary/metadata.json",
"modality": "Biomedical text",
"n_dims": 768,
"n_genes": 19168,
"name": "BioBERT (gene+protein text)",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/biobert_gene_protein_summary",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BioConceptVec-CBOW.",
"category": "Biomedical text",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 84.79,
"dataset_path": "data/embeddings/bioconceptvec_cbow",
"description": "Biomedical concept representation for genes learned from PubMed text with the BioConceptVec CBOW model.",
"id": "bioconceptvec_cbow",
"license": "Original source / license terms",
"license_url": "https://github.com/ncbi/BioConceptVec",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_cbow/metadata.json",
"modality": "Biomedical text",
"n_dims": 100,
"n_genes": 17040,
"name": "BioConceptVec CBOW",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_cbow",
"qc_status": "legacy_core_package",
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"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BioConceptVec-fastText.",
"category": "Biomedical text",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 84.79,
"dataset_path": "data/embeddings/bioconceptvec_fasttext",
"description": "Biomedical concept representation for genes learned from PubMed text with the BioConceptVec fastText model.",
"id": "bioconceptvec_fasttext",
"license": "Original source / license terms",
"license_url": "https://github.com/ncbi/BioConceptVec",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_fasttext/metadata.json",
"modality": "Biomedical text",
"n_dims": 100,
"n_genes": 17040,
"name": "BioConceptVec fastText",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_fasttext",
"qc_status": "legacy_core_package",
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"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
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},
{
"algorithm": "BioConceptVec-GloVe.",
"category": "Biomedical text",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 84.79,
"dataset_path": "data/embeddings/bioconceptvec_glove",
"description": "Biomedical concept representation for genes learned from PubMed text with the BioConceptVec GloVe model.",
"id": "bioconceptvec_glove",
"license": "Original source / license terms",
"license_url": "https://github.com/ncbi/BioConceptVec",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_glove/metadata.json",
"modality": "Biomedical text",
"n_dims": 100,
"n_genes": 17040,
"name": "BioConceptVec GloVe",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_glove",
"qc_status": "legacy_core_package",
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"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BioConceptVec-SkipGram.",
"category": "Biomedical text",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 84.79,
"dataset_path": "data/embeddings/bioconceptvec_skipgram",
"description": "Biomedical concept representation for genes learned from PubMed text with the BioConceptVec Skip-Gram model.",
"id": "bioconceptvec_skipgram",
"license": "Original source / license terms",
"license_url": "https://github.com/ncbi/BioConceptVec",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_skipgram/metadata.json",
"modality": "Biomedical text",
"n_dims": 100,
"n_genes": 17040,
"name": "BioConceptVec Skip-Gram",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/bioconceptvec_skipgram",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "BLAST.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 93.91,
"dataset_path": "data/embeddings/probe_blast",
"description": "Protein sequence-similarity representation based on BLAST-derived similarity features against a reference protein set.",
"id": "probe_blast",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_blast/metadata.json",
"modality": "Protein sequence",
"n_dims": 20421,
"n_genes": 18873,
"name": "BLAST",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_blast",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "CPC-PROT.",
"category": "Protein sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 91.82,
"dataset_path": "data/embeddings/probe_cpc_prot",
"description": "Protein sequence representation produced by CPCProt, a recurrent self-supervised protein model.",
"id": "probe_cpc_prot",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_cpc_prot/metadata.json",
"modality": "Protein sequence",
"n_dims": 512,
"n_genes": 18453,
"name": "CPC-PROT",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_cpc_prot",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "deepNF: deep network fusion for protein function prediction.",
"category": "Networks",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 94.89,
"dataset_path": "data/embeddings/deepnf_string_v12_ppmi_svd_fusion",
"description": "Network representation built from STRING v12 interaction evidence channels using PPMI/SVD features and fusion into a shared low-dimensional gene space.",
"id": "deepnf_string_v12_ppmi_svd_fusion",
"license": "Original source / license terms",
"license_url": "https://string-db.org/cgi/download?species_text=Homo+sapiens",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/deepnf_string_v12_ppmi_svd_fusion/metadata.json",
"modality": "Networks",
"n_dims": 256,
"n_genes": 19071,
"name": "deepNF (STRING)",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/deepnf_string_v12_ppmi_svd_fusion",
"qc_status": "legacy_core_package",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "DeepSEA sequence-to-function model (checkpoint: DeepSEA/predict from Kipoi).",
"category": "Regulatory genomics",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 100.0,
"dataset_path": "data/embeddings/deepsea_tss_1kb_epigenomic_probs",
"description": "Regulatory representation from DeepSEA-predicted chromatin and transcription-factor features for 1-kb DNA windows centered at gene transcription start sites.",
"id": "deepsea_tss_1kb_epigenomic_probs",
"license": "Original source / license terms",
"license_url": "https://kipoi.org/models/DeepSEA/predict/",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/deepsea_tss_1kb_epigenomic_probs/metadata.json",
"modality": "Regulatory genomics",
"n_dims": 919,
"n_genes": 20097,
"name": "DeepSEA",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/deepsea_tss_1kb_epigenomic_probs",
"qc_status": "processed_qc_passed",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "DepMap CRISPR gene-effect matrix. Gene-effect matrix transposed to genes x models; missing values filled by source gene mean; model/cell-line features standardized across mapped genes; PCA with randomized SVD.",
"category": "Functional",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 92.36,
"dataset_path": "data/embeddings/depmap_crispr_gene_effect_pca256",
"description": "Functional gene representation derived from DepMap CRISPR gene-effect profiles across cancer cell lines, standardized and reduced to 256 dimensions with PCA.",
"id": "depmap_crispr_gene_effect_pca256",
"license": "Original source / license terms",
"license_url": "https://depmap.org/portal/download/",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/depmap_crispr_gene_effect_pca256/metadata.json",
"modality": "Functional",
"n_dims": 256,
"n_genes": 18562,
"name": "DepMap CRISPR",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/depmap_crispr_gene_effect_pca256",
"qc_status": "processed_qc_passed",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "DNABERT-2 genome language model (checkpoint: DNABERT-2 117M). Mean pooling over final-layer non-special token embeddings. Long CDS sequences split into nucleotide chunks, then length-weighted mean of chunk-level embeddings.",
"category": "DNA sequence",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 99.94,
"dataset_path": "data/embeddings/dnabert2_117m_cds",
"description": "Gene representation from canonical coding DNA sequences encoded with DNABERT-2, using pooled sequence features to obtain one vector per gene.",
"id": "dnabert2_117m_cds",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/zhihan1996/DNABERT-2-117M",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/dnabert2_117m_cds/metadata.json",
"modality": "DNA sequence",
"n_dims": 768,
"n_genes": 20085,
"name": "DNABERT-2",
"package_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/tree/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/dnabert2_117m_cds",
"qc_status": "processed_qc_passed",
"source_dataset": "BoevaLab/Gene-Embedding-Hub-dataset",
"source_revision": "326b257a20e73df337ece40bb82401e277851f5b",
"zero_vectors": 0
},
{
"algorithm": "TransE_l2 knowledge graph embedding",
"category": "Knowledge graph",
"category_source": "manual",
"community_contribution": false,
"coverage_pct": 94.54,
"dataset_path": "data/embeddings/drkg_transe",
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"license": "Apache-2.0 for the DRKG project; underlying source licenses apply",
"license_note": "DRKG's official source-license table includes CC BY-NC, non-commercial, and mixed-license inputs. Redistribution and use of this derived embedding must comply with all applicable source terms; do not assume unrestricted commercial use.",
"license_url": "https://github.com/gnn4dr/DRKG/blob/d4bb1974312013c4bd79a13e42c1d9492033f8c7/LICENSE",
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"algorithm": "Enformer sequence-to-function model. mean of central 3 human output bins around TSS. log1p raw human central3 mean tracks, then dimension-wise z-score across genes.",
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"id": "enformer_tss_393kb_human_central3",
"license": "Original source / license terms",
"license_url": "https://tfhub.dev/deepmind/enformer/1",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/enformer_tss_393kb_human_central3/metadata.json",
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"id": "probe_esmb1",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
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"algorithm": "ESM2 Transformer (checkpoint: esm2_t33_650M_UR50D, as reported by Zhong). mean pooling over amino-acid token embeddings, as reported by Zhong.",
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"community_contribution": false,
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"description": "Protein sequence representation from ESM-2 650M, obtained by pooling residue-level embeddings into one vector per human protein/gene.",
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"license": "Original source / license terms",
"license_url": "https://zenodo.org/records/16764517",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/esm2/metadata.json",
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"algorithm": "Gene2Vec.",
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"description": "Gene co-expression representation learned with Gene2Vec from gene co-occurrence patterns in large-scale expression data.",
"id": "gene2vec",
"license": "Original source / license terms",
"license_url": "https://github.com/jingcheng-du/Gene2vec",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/gene2vec/metadata.json",
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"algorithm": "GeneCompass Base, 12-layer knowledge-informed cross-species foundation model. none; static learned token embedding weight extracted directly from checkpoint.",
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"id": "genecompass_base_gene_token",
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"license_url": "https://github.com/xCompass-AI/GeneCompass",
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"algorithm": "Geneformer V2 316M input gene-token embedding layer. none; static learned token embedding weight extracted directly from checkpoint.",
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"id": "genept_ada_conservative",
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"algorithm": "GenePT-Model3-GeneProtein.",
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"algorithm": "binary GO Biological Process profile + TF-IDF + TruncatedSVD-256",
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"id": "go_bp_uniprot_tfidf_svd256",
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"license_note": "Attribution, release identification, license notice, change notice, source link, and warranty disclaimer are required by CC BY 4.0 and the Gene Ontology citation policy; these are included in the package.",
"license_url": "https://creativecommons.org/licenses/by/4.0/",
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"algorithm": "GTEx RNA-seq median tissue expression matrix. TPM values were transformed with log1p, then each tissue column was z-scored across mapped genes.",
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"license": "Original source / license terms",
"license_url": "https://gtexportal.org/home/datasets",
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"algorithm": "Helix-mRNA (checkpoint: helical-ai/helix-mRNA). final token embeddings from official Helix wrapper, mean pooled over non-special sequence tokens; long transcripts chunked and token-count weighted.",
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"algorithm": "Hetionet v1.0 heterogeneous biomedical knowledge graph. Built sparse gene x typed-neighbor-feature count matrix; collapsed duplicate gene-feature edges by summation; applied sublinear TF-IDF weighting; reduced with randomized TruncatedSVD; z-scored SVD dimensions across source genes; mapped to master by Entrez ID.",
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"license": "Original source / license terms",
"license_url": "https://zenodo.org/records/268568",
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"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
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"algorithm": "HPA subcellular immunofluorescence annotations. Main location, Additional location, and Extracellular location were parsed as semicolon-separated labels. A binary multi-hot location vector was created per gene, duplicate genes were collapsed by max, then location columns were z-scored across mapped genes.",
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"id": "hpa_subcellular_location_multihot_zscore",
"license": "Original source / license terms",
"license_url": "https://www.proteinatlas.org/humanproteome/subcellular/data",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/hpa_subcellular_location_multihot_zscore/metadata.json",
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"algorithm": "HPA normal tissue immunohistochemistry annotations. maximum ordinal protein-expression level across annotated cell types within each tissue. HPA Level encoded as Not detected=0, Low=1, Medium=2, High=3. For each gene and tissue, maximum level across annotated cell types was used. Tissue columns were z-scored across mapped genes.",
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"license": "Original source / license terms",
"license_url": "https://www.proteinatlas.org/humanproteome/tissue/data",
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"algorithm": "HyenaDNA medium 160k sequence-length checkpoint (checkpoint: LongSafari/hyenadna-medium-160k-seqlen-hf). final hidden-state mean pooling over non-special nucleotide tokens; long CDS split into chunks and token-count weighted.",
"category": "DNA sequence",
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"description": "Protein sequence representation learned with the unsupervised Learned-Vec method from amino-acid sequence context.",
"id": "probe_learned_vec",
"license": "Original source / license terms",
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"community_contribution": false,
"coverage_pct": 94.13,
"dataset_path": "data/embeddings/mahi_all_contexts_mean",
"description": "Multimodal gene representation obtained by averaging Mahi embeddings across tissue and cell contexts, each integrating context-specific network topology with chromatin-accessibility, transcription-factor-binding, histone-modification, and protein-structure information.",
"id": "mahi_all_contexts_mean",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/datasets/anushaggs/Mahi_gene_embeddings",
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"coverage_pct": 94.13,
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"description": "Multimodal gene representation from the global Mahi context, integrating global network topology with chromatin-accessibility, transcription-factor-binding, histone-modification, and protein-structure information.",
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"license": "Original source / license terms",
"license_url": "https://huggingface.co/datasets/anushaggs/Mahi_gene_embeddings",
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"algorithm": "Mashup-STRING.",
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"community_contribution": false,
"coverage_pct": 81.26,
"dataset_path": "data/embeddings/mashup_string",
"description": "PPI/network representation learned with Mashup from STRING interaction networks, preserving integrated network diffusion structure in a low-dimensional gene space.",
"id": "mashup_string",
"license": "Original source / license terms",
"license_url": "https://mashup.csail.mit.edu/",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/mashup_string/metadata.json",
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"description": "Protein sequence representation from the FAIR-ESM MSA Transformer using depth-1 single-sequence inputs, pooled to one vector per human protein/gene.",
"id": "msa_transformer_depth1_uniprot_human",
"license": "Original source / license terms",
"license_url": "https://github.com/facebookresearch/esm",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/msa_transformer_depth1_uniprot_human/metadata.json",
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"description": "Multimodal gene representation learned from mutation profiles together with biomedical literature and protein-protein interaction information.",
"id": "probe_mut2vec",
"license": "Original source / license terms",
"license_url": "https://huggingface.co/spaces/HUBioDataLab/PROBE",
"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/probe_mut2vec/metadata.json",
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"algorithm": "NEWT component embedding (checkpoint: not applicable). precomputed component embedding table.",
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"coverage_pct": 87.07,
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"description": "NEWT network representation derived from normalized CellNet tissue- and lineage-specific regulatory-network connectivity scores, encoding each gene’s association with lineage programs.",
"id": "newt_cellnet",
"license": "Original source / license terms",
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"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/newt_cellnet/metadata.json",
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"description": "NEWT component representation derived from the Gene Ontology graph, encoding graph-based functional relationships among genes.",
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"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/newt_go_graph/metadata.json",
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"description": "NEWT component representation derived from Gene Ontology annotations and provided as a 256-dimensional functional gene embedding.",
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"license": "Original source / license terms",
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"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/newt_go_256/metadata.json",
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"coverage_pct": 76.74,
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"description": "NEWT component representation derived from MSigDB pathway and gene-set memberships, encoding shared functional-program context among genes.",
"id": "newt_msigdb_bundle",
"license": "Original source / license terms",
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"metadata_url": "https://huggingface.co/datasets/BoevaLab/Gene-Embedding-Hub-dataset/blob/326b257a20e73df337ece40bb82401e277851f5b/data/embeddings/newt_msigdb_bundle/metadata.json",
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"algorithm": "node2vec: scalable feature learning for networks.",
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"coverage_pct": 91.34,
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"description": "Protein-interaction network representation learned with node2vec on a consensus human protein-protein interaction graph.",
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"license": "Original source / license terms",
"license_url": "https://github.com/ylaboratory/gene-embedding-benchmarks",
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{
"algorithm": "Nucleotide Transformer v2 (checkpoint: InstaDeepAI/nucleotide-transformer-v2-500m-multi-species). final hidden layer mean pooling over non-special, non-padding tokens; chunk-weighted by token count for long CDS.",
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"dataset_path": "data/embeddings/nt_v2_500m_multispecies_cds",
"description": "Gene representation from canonical coding DNA sequences encoded with Nucleotide Transformer v2 500M multi-species and pooled into one vector per gene.",
"id": "nt_v2_500m_multispecies_cds",
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"description": "Ontology representation generated with OPA2Vec from Gene Ontology structure and human GO annotations, embedding genes from their semantic annotation context.",
"id": "opa2vec_goa_human",
"license": "Original source / license terms",
"license_url": "https://github.com/bio-ontology-research-group/opa2vec",
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"algorithm": "Orthrus mature RNA foundation model (checkpoint: Orthrus base 4-track). Orthrus representation() sequence-level embedding. For transcripts longer than max_full_len, length-weighted mean of chunk-level Orthrus embeddings.",
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"id": "primekg_nonppi_biomedical_context_tfidf_svd256",
"license": "Original source / license terms",
"license_url": "https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IXA7BM",
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