| --- |
| license: cc-by-4.0 |
| tags: |
| - virtual-screening |
| - drug-discovery |
| - aidd |
| - knowledge-graph |
| - parkinsons-disease |
| - toxicology |
| pretty_name: Biopharma hackathon data |
| configs: |
| - config_name: targets |
| data_files: targets.parquet |
| - config_name: pockets |
| data_files: pockets.parquet |
| - config_name: pocket_structures |
| data_files: pocket_structures.parquet |
| - config_name: molecules |
| data_files: molecules.parquet |
| - config_name: hits |
| data_files: hits.parquet |
| - config_name: pd_subgraph |
| data_files: pd_subgraph.parquet |
| - config_name: pd_subgraph_enrichment |
| data_files: pd_subgraph_enrichment.parquet |
| - config_name: pd_tree |
| data_files: pd_tree.parquet |
| - config_name: pd_toxin_target |
| data_files: pd_toxin_target.parquet |
| - config_name: pd_toxin_pathway |
| data_files: pd_toxin_pathway.parquet |
| --- |
| |
| # Biopharma hackathon data |
|
|
| Two independent datasets share this repo. They have different sources and different licenses, |
| and nothing joins them: |
|
|
| 1. **[GenomeScreen (relational)](#1-genomescreen-as-a-relational-dataset)** — 5 tables, the |
| DrugCLIP genome-wide virtual screen parsed into parquet. |
| 2. **[Parkinson's disease subgraph](#2-parkinsons-disease-subgraph-primekg)** — 5 tables, a |
| pathway-centric neighbourhood extracted from PrimeKG, as a graph and as a |
| disease→pathway→protein→drug tree, plus an environmental-toxin overlay on the same |
| pathways. |
|
|
| --- |
|
|
| # 1. GenomeScreen, as a relational dataset |
|
|
| A normalized, queryable form of [GenomeScreen](https://huggingface.co/datasets/THU-ATOM/GenomeScreenDB) |
| — the DrugCLIP genome-wide virtual screen of human proteins. The source release ships as |
| ~26.5k per-pocket directories of CSV and structure files; this is the same data parsed into |
| five parquet tables. |
|
|
| **This contains the screen results only.** The 27 GB of `.pdbgz` receptor structures are not |
| redistributed here; `pocket_structures` records their paths relative to the source release's |
| `screen_results/` directory so you can join against your own copy. |
|
|
| ## Tables |
|
|
| | file | rows | grain | |
| | --- | ---: | --- | |
| | `targets.parquet` | 9,919 | UniProt accession | |
| | `pockets.parquet` | 26,562 | screen-result directory (target × AF fragment × pocket) | |
| | `pocket_structures.parquet` | 215,481 | refined receptor conformation + docking grid | |
| | `molecules.parquet` | 1,110,392 | distinct `(oid, smiles)` — 982,239 distinct compounds | |
| | `hits.parquet` | 3,175,634 | `(pocket, molecule)` DrugCLIP hit | |
|
|
| Join keys: `hits.pocket_key → pockets.pocket_key → targets.uniprot_acc`, and |
| `hits.mol_id → molecules.mol_id`. |
|
|
| ## How the data is keyed |
|
|
| - **Targets are UniProt accessions**, wrapped in AlphaFold DB ids. A `pocket_key` of |
| `AF-Q12879-F1-model_v4_0_pocket3` is UniProt `Q12879`, AF2 model fragment `0`, pocket `3`. |
| - **Pockets come in two kinds.** `pocket_kind = 'detected'` (22,061) were found by apo pocket |
| detection; `'template'` (4,501) were transferred from an aligned holo structure and keep the |
| source PDB id in `pocket_structures.template_pdb_id` (3,125 distinct entries). |
| - **Molecules are vendor catalogue ids plus SMILES — there are no PubChem CIDs.** `oid` is a |
| ZINC id (`ZINC000066055208`), an Enamine REAL id (`Z1333761449_1_T2`) or an Enamine PV id |
| (`PV-001914042032_1_T1`). The Enamine `_<protomer>_T<tautomer>` suffix is split into |
| `protomer_idx` / `tautomer_idx`; `catalog_id` strips it. |
| - **The same `oid` sometimes carries two SMILES** (protonation variants), so the `molecules` |
| grain is `(oid, smiles)` behind a surrogate `mol_id`. Group by `catalog_id` to collapse them. |
| - **`hits.source_index` is not a molecule id.** It is the source `leader.csv` `Name` column, a |
| row index into the screened library that differs between targets for the same compound. Use |
| `catalog_id` to identify a compound. `rank_in_pocket` (1 = best) is derived from `score`. |
| |
| ## Caveats worth knowing before you use it |
| |
| - **These are predicted hits, not measured interactions.** `score` is DrugCLIP similarity. |
| - **Scores are not calibrated across proteins.** Ranking is meaningful within a pocket; a |
| global threshold across targets is not. |
| - **The per-molecule view is truncated by construction.** The screen kept each *pocket's* top |
| 10K, then cluster leaders — so a molecule's known target is not guaranteed to survive that |
| cut. Empirically it often doesn't: ibuprofen appears with 34 predicted targets, none of them |
| COX-1 or COX-2, though both are screened. Read a molecule's row set as a biased sample of |
| its profile, not as its profile. |
| - **It is a make-on-demand screening library, not a drug library.** Most approved drugs are |
| absent entirely. |
|
|
| ## Usage |
|
|
| ```python |
| import duckdb |
| conn = duckdb.connect() |
| conn.execute(""" |
| CREATE VIEW hits AS SELECT * FROM 'hf://datasets/conradry/biopharma-hackathon/hits.parquet'; |
| CREATE VIEW pockets AS SELECT * FROM 'hf://datasets/conradry/biopharma-hackathon/pockets.parquet'; |
| CREATE VIEW molecules AS SELECT * FROM 'hf://datasets/conradry/biopharma-hackathon/molecules.parquet'; |
| """) |
| |
| # top hits for a UniProt accession, one row per compound |
| conn.execute(""" |
| SELECT m.catalog_id, m.source, max(h.score) AS score, |
| arg_max(m.smiles, h.score) AS smiles |
| FROM hits h |
| JOIN pockets p ON p.pocket_key = h.pocket_key |
| JOIN molecules m ON m.mol_id = h.mol_id |
| WHERE p.uniprot_acc = ? |
| GROUP BY 1, 2 ORDER BY score DESC LIMIT 25 |
| """, ["P14416"]).fetchall() |
| ``` |
|
|
| ## Source and license |
|
|
| Derived from **GenomeScreen** by Jia et al., released under CC-BY-4.0. This derived dataset |
| carries the same license, and the original work must be credited: |
|
|
| > Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., |
| > Huang, Y., Deng, Z., Jin, Y., Yuan, Y., Tian, J., He, W., Ma, W., Zhang, Y., Yan, C., Liu, L., |
| > Zhang, W., Lan, Y. *Deep contrastive learning enables genome-wide virtual screening.* |
| > bioRxiv 2024.09.02.610777. |
|
|
| - Source dataset: https://huggingface.co/datasets/THU-ATOM/GenomeScreenDB |
| - Portal: https://drug-the-whole-genome.yanyanlan.com |
| - Code: https://github.com/THU-ATOM/Drug-The-Whole-Genome |
|
|
| ```bibtex |
| @article{Jia2024GenomeScreen, |
| title={Deep contrastive learning enables genome-wide virtual screening}, |
| author={Jia, Yinjun and Gao, Bowen and Tan, Jiaxin and Zheng, Jiqing and Hong, Xin and Zhu, Wenyu and Tan, Haichuan and Xiao, Yuan and Tan, Liping and Cai, Hongyi and Huang, Yanwen and Deng, Zhiheng and Jin, Yue and Yuan, Yafei and Tian, Jiekang and He, Wei and Ma, Weiying and Zhang, Yaqin and Yan, Chuangye and Liu, Lei and Zhang, Wei and Lan, Yanyan}, |
| journal={bioRxiv}, |
| year={2024}, |
| doi={10.1101/2024.09.02.610777} |
| } |
| ``` |
|
|
| --- |
|
|
| # 2. Parkinson's disease subgraph (PrimeKG) |
|
|
| A pathway-centric neighbourhood of Parkinson's disease extracted from |
| [PrimeKG](https://github.com/mims-harvard/PrimeKG) (13,001,666 edges), plus a pathway |
| enrichment table for filtering it down. |
|
|
| | file | rows | grain | |
| | --- | ---: | --- | |
| | `pd_subgraph.parquet` | 162,384 | edge (both orientations — see below) | |
| | `pd_subgraph_enrichment.parquet` | 283 | pathway | |
| | `pd_tree.parquet` | 54,557 | root→leaf path, disease → pathway → protein → drug | |
| | `pd_toxin_target.parquet` | 79 | (environmental toxin, gene) | |
| | `pd_toxin_pathway.parquet` | 76 | pathway, with toxin evidence rolled up | |
|
|
| ## How it was built |
|
|
| **PrimeKG has no `disease_pathway` relation**, so the disease is bridged to pathways through |
| its associated proteins: |
| |
| ``` |
| Parkinson disease (x_index 80275) |
| --disease_protein--> 104 seed proteins |
| --pathway_protein--> 283 pathways |
| --pathway_pathway--> edges among those pathways |
| --pathway_protein--> 4,916 proteins in those pathways |
| --protein_protein--> edges among those proteins |
| ``` |
| |
| The `layer` column tags which step each edge came from: |
| |
| | `layer` | rows | unique edges | |
| | --- | ---: | ---: | |
| | `disease_protein` | 208 | 104 | |
| | `pathway_pathway` | 62 | 31 | |
| | `pathway_protein` | 24,544 | 12,272 | |
| | `protein_protein` | 137,570 | 68,785 | |
| |
| PD is the merged MONDO/Orphanet node at `x_index` 80275. The other node named "Parkinson |
| disease" (`x_index` 91594, MONDO:0005180) carries only `disease_disease` edges and no protein |
| links. |
| |
| Columns are PrimeKG's original twelve (`relation`, `display_relation`, `x_index`, `x_id`, |
| `x_type`, `x_name`, `x_source`, and the `y_*` equivalents) plus `layer`, |
| `x_pathway_qvalue` and `y_pathway_qvalue`. |
| |
| ## Pathway enrichment |
| |
| Bridging through proteins pulls in generic pathways (`Platelet degranulation`) alongside |
| PD-relevant ones, so each of the 283 pathways is scored by a hypergeometric test: background = |
| the 10,849 pathway-annotated proteins in PrimeKG, sample = the 90 of 104 seed proteins that |
| carry a pathway annotation. P-values are Benjamini-Hochberg corrected across all 283 pathways. |
| **21 pathways reach q < 0.05.** |
| |
| `pd_subgraph_enrichment.parquet` holds `pathway_index`, `pathway_name`, `pathway_size`, |
| `seed_overlap`, `expected_overlap`, `fold_enrichment`, `pvalue`, `qvalue`, ranked by q-value. |
| The same q-value is denormalized onto the edge table as `x_pathway_qvalue` / |
| `y_pathway_qvalue` — populated on whichever endpoint is a pathway, null otherwise (a |
| `pathway_pathway` edge has both). |
| |
| Top hits are recognisable PD biology: |
| |
| | pathway | size | overlap | fold | q | |
| | --- | ---: | ---: | ---: | ---: | |
| | Interleukin-4 and Interleukin-13 signaling | 108 | 7 | 7.8 | 6.4e-03 | |
| | Biogenic amines are oxidatively deaminated to aldehydes | 2 | 2 | 120.5 | 6.4e-03 | |
| | Detoxification of Reactive Oxygen Species | 37 | 4 | 13.0 | 1.5e-02 | |
| | Catecholamine biosynthesis | 4 | 2 | 60.3 | 1.6e-02 | |
| | Dopamine receptors | 5 | 2 | 48.2 | 2.1e-02 | |
| | Transcriptional activation of mitochondrial biogenesis | 56 | 4 | 8.6 | 2.4e-02 | |
| |
| ## The tree view |
| |
| `pd_tree.parquet` is the same neighbourhood reshaped into a tree, with drugs attached below |
| the proteins: |
| |
| ``` |
| Parkinson disease (root) |
| └── 283 pathways |
| └── 4,916 proteins (12,272 pathway-protein pairs) |
| └── 4,993 drugs (drug_protein edges) |
| ``` |
| |
| **PrimeKG has no `drug_pathway` relation either**, so proteins are the only join between drugs |
| and pathways — a drug hangs off the tree once per (pathway, protein) pair it reaches. Use |
| `COUNT(DISTINCT drug_index)` when you want unique drugs. |
| |
| One row per root-to-leaf path, fully denormalized, so any level rolls up with a `GROUP BY`: |
| `disease_index`/`disease_name`, `pathway_index`/`pathway_name`/`pathway_size`/ |
| `pathway_seed_overlap`/`pathway_fold_enrichment`/`pathway_qvalue`, `protein_index`/ |
| `protein_name`/`protein_is_pd_seed`, `drug_index`/`drug_id`/`drug_name`/`drug_relation`, and |
| `depth`. Proteins with no drug still get a row (`depth = 3`, drug columns null; 7,917 of them) |
| so the protein layer stays complete. |
| |
| Nothing is pre-filtered — the narrowings you are likely to want are all columns: |
| `pathway_qvalue < 0.05` (21 pathways), `protein_is_pd_seed`, `drug_relation = 'target'`. |
| |
| **`drug_relation` matters more than it looks.** Only `target` (30,151 rows) is a therapeutic |
| target; `enzyme` (10,149), `transporter` (3,680) and `carrier` (2,660) are ADME relationships |
| — a metabolizing CYP is not a repurposing lead. Note that 185 drug-protein pairs legitimately |
| carry two relation types (usually enzyme + target), so they appear as two rows. |
|
|
| The tree drops `protein_protein` and `pathway_pathway`, which have no place in a tree; use |
| `pd_subgraph.parquet` for those. |
|
|
| ## The environmental-toxin overlay |
|
|
| A second, independent line of evidence on the same pathways. Ten environmental toxins with |
| an established PD association (paraquat, rotenone, MPTP, manganese, lead, mercury, |
| chlorpyrifos, dieldrin, trichloroethylene, tetrachloroethylene) were mapped to their |
| literature-co-mentioned gene targets, and those genes joined onto the tree's pathways. |
|
|
| The premise: a toxin that is *causative* for PD implicates the pathways it perturbs, so a |
| pathway reached by both the disease-association route and the toxicological route carries a |
| stronger prior than either alone. **9 of the 21 enriched pathways contain a toxin target**, |
| and the top of that list is the canonical PD tox story. |
|
|
| `pd_toxin_target.parquet` is one row per (toxin, gene): `toxin`, `cid`, `pubmed_mentions`, |
| `gene_symbol`, `protein_index` (PrimeKG), `evidence_count`, `n_source_entries`, |
| `source_entries`, `mapping_confidence`, `toxin_mapped_fraction`, `in_pd_tree`, `is_pd_seed`, |
| `tf`, `idf`, `specificity_weight`. |
|
|
| `pd_toxin_pathway.parquet` rolls that up per pathway and joins to `pd_tree.parquet` on |
| `pathway_index`: `n_toxins`, `n_toxin_targets`, `toxin_support`, `toxins`, `toxin_targets`, |
| alongside the pathway's `pathway_qvalue`, `n_drugs` and `n_target_drugs`. |
|
|
| Ranked by toxin support among pathways at q < 0.05: |
|
|
| | pathway | support | q | toxins | targets | |
| | --- | ---: | ---: | ---: | --- | |
| | Detoxification of Reactive Oxygen Species | 3.28 | 1.5e-02 | 6 | CAT, GPX1–3, SOD1–3 | |
| | Transcriptional activation of mitochondrial biogenesis | 2.99 | 2.4e-02 | 7 | GABPA, PPARGC1B, SOD2 | |
| | FOXO-mediated transcription of oxidative stress genes | 1.98 | 3.3e-02 | 6 | CAT, SOD2 | |
| | Interleukin-4 and Interleukin-13 signaling | 1.24 | 6.4e-03 | 4 | AKT1, BCL2, IL6, MAOA, TNF | |
| | Catecholamine biosynthesis | 1.05 | 1.6e-02 | 2 | TH | |
| | Biogenic amines deaminated by MAOA and MAOB | 0.19 | 6.4e-03 | 1 | MAOA, MAOB | |
|
|
| ### How the targets were mapped |
|
|
| The source lists targets as `name [TAG] (count)`, where TAG is a gene symbol *or* an EC |
| number. Three properties of that format shape the table: |
|
|
| - **The EC entries carry the mechanism.** MPTP's causal target is monoamine oxidase, which |
| appears only as `EC:1.4.3.4`; rotenone's is complex I, only as `EC:1.6.99.3`. Symbol-only |
| parsing loses both. EC classes are expanded through a hand-curated map, and |
| `mapping_confidence` records how firm each expansion is: `exact` (one human gene), |
| `family` (a small enumerated family — MAOA/MAOB, SOD1–3), or `complex` (complex I core |
| subunits). Large cross-species classes (`peroxidase`, `unspecific monooxygenase`) and |
| non-human ones (photosystem II, lignin peroxidase, bacterial dehalogenases) are dropped |
| rather than guessed at. |
| - **A toxin often lists one protein twice**, once by symbol and once by EC — lead has both |
| `catalase [CAT] (576)` and `catalase [EC:1.11.1.6] (234)`. Rows are aggregated to the |
| (toxin, gene) grain and the counts summed; `source_entries` preserves what contributed. |
| A family EC has its evidence split across members rather than replicated, so a family tag |
| cannot outvote a specific symbol. |
| - **The gene lists are cross-species.** Influenza PB2, bacterial MERA, rice LOC4326471 and a |
| *Drosophila* GABA receptor all appear. Only symbols resolving to a PrimeKG `gene/protein` |
| node are kept. |
|
|
| `specificity_weight` is `tf × idf`: a gene's share of its toxin's mapped evidence, damped by |
| how many toxins name it. CAT and the SODs appear for 6 of the 10 toxins — they are the |
| background of the toxicology literature, not a toxin-specific signal, and the weighting is |
| what keeps them from dominating. |
|
|
| ## Caveats worth knowing before you use it |
|
|
| - **Every edge in `pd_subgraph.parquet` appears twice.** PrimeKG stores undirected edges in |
| both orientations, and this extract preserves that verbatim — all 162,384 rows are 81,192 |
| unique edges mirrored. Filter `x_index < y_index` for a single copy, and match on |
| `x_type`/`y_type` rather than assuming which column holds the pathway or the disease. |
| (`pd_tree.parquet` is already deduplicated — it has a fixed level order.) |
| - **The disease→pathway link is inferred, not asserted.** These pathways are not curated PD |
| pathways; they are pathways containing at least one PD-associated protein. |
| - **14 of the 104 seed proteins have no pathway annotation** and are excluded from the |
| enrichment test. They remain in the edge table under the `disease_protein` layer. |
| - **Small pathways produce fragile scores.** Several top hits rest on an overlap of 2 in a |
| pathway of size 2–6. `Signaling by Insulin receptor` at size 2 is a PrimeKG annotation |
| artifact, not the real Reactome pathway. Add `pathway_size >= 10` for robust hits only. |
| - **Enrichment is scored over the 283 bridged pathways, not all of PrimeKG.** The multiple |
| testing correction is relative to that set. |
| - **Ranking drugs by pathway or target count rewards promiscuity.** The query above returns |
| zinc salts, cannabidiol and fatty acids at the top — compounds with dozens of annotated |
| protein interactions, not compounds with a PD rationale. Weight by target specificity, or |
| restrict to a pathway you care about, before reading anything into the ordering. |
|
|
| - **PrimeKG's drug nodes are all of DrugBank, and it carries no approval status.** Every |
| `drug_id` is a `DB…` accession, but the set includes experimental entries, cofactors and |
| crystallographic ligands — NADH, copper and glutathione adducts all appear as `target` |
| drugs. There is no column to filter on; join against a DrugBank approval list if you need |
| approved compounds only. |
|
|
| Specific to the toxin overlay: |
|
|
| - **A shared target does not mean a shared direction of effect, and PrimeKG cannot tell you |
| which.** `drug_relation` distinguishes target from enzyme, never agonist from antagonist. |
| If a toxin causes PD by inhibiting a protein, a drug that also inhibits it is a risk, not a |
| therapy. `SLC6A3` is the clearest case: DAT-mediated uptake is how MPP+ enters dopaminergic |
| neurons, so a DAT blocker is protective while amphetamine — on the same target list — is |
| not. Read the overlay as nominating *pathways*, and get direction from DrugBank's action |
| field before selecting compounds within one. |
| - **The counts are literature co-mentions, not affinities or assays.** A high count means the |
| toxin and the gene are frequently discussed together, which tracks how well-studied a gene |
| is at least as much as how relevant it is. |
| - **The source lists only the top 8 of 25 targets per toxin.** Document frequencies — and so |
| every `idf` — are computed over that truncated view, which biases `idf` upward for genes |
| that fall below the cut for some toxins. |
| - **Two toxins are mostly unmappable.** `toxin_mapped_fraction` is 0.38 for tetrachloroethylene |
| and 0.40 for trichloroethylene; the rest of their lists is bacterial or fungal. Because `tf` |
| is a share of *mapped* evidence, the survivors absorb the whole share and their weights are |
| inflated. Treat anything below ~0.5 as soft. |
| - **`toxin_support` is a raw sum, so it rewards large pathways.** Unfiltered, `Neutrophil |
| degranulation` (478 proteins, q = 0.21) tops the ranking. The enrichment q-value already |
| controls for size — filter on it first, then rank by support. |
| - **10 toxin targets are absent from the tree entirely**, including all seven complex I core |
| subunits — the most PD-relevant mechanism in the set. They resolve to valid PrimeKG |
| `protein_index` values but sit in no PD-bridged pathway, so they contribute nothing to |
| `pd_toxin_pathway.parquet`. Filter `in_pd_tree = false` to see them. |
| |
| ## Usage |
| |
| ```python |
| import duckdb |
| conn = duckdb.connect() |
| base = "hf://datasets/conradry/biopharma-hackathon" |
| |
| # significantly enriched pathways and the proteins in them, deduplicated |
| conn.execute(f""" |
| SELECT DISTINCT |
| CASE WHEN x_type = 'pathway' THEN x_name ELSE y_name END AS pathway, |
| CASE WHEN x_type = 'pathway' THEN y_name ELSE x_name END AS protein |
| FROM '{base}/pd_subgraph.parquet' |
| WHERE layer = 'pathway_protein' |
| AND coalesce(x_pathway_qvalue, y_pathway_qvalue) < 0.05 |
| AND x_index < y_index |
| """).fetchall() |
| |
| # repurposing candidates: drugs targeting proteins in enriched PD pathways, |
| # ranked by how many distinct enriched pathways they reach |
| conn.execute(f""" |
| SELECT drug_name, |
| count(DISTINCT pathway_index) AS pathways, |
| count(DISTINCT protein_index) AS proteins, |
| string_agg(DISTINCT protein_name, ', ') AS targets |
| FROM '{base}/pd_tree.parquet' |
| WHERE drug_relation = 'target' AND pathway_qvalue < 0.05 |
| GROUP BY 1 ORDER BY pathways DESC, proteins DESC LIMIT 25 |
| """).fetchall() |
| |
| # convergent evidence: drugs targeting proteins in pathways that are both |
| # statistically enriched for PD proteins and hit by a PD-associated toxin |
| conn.execute(f""" |
| SELECT t.drug_name, t.protein_name, x.pathway_name, |
| x.toxins, x.toxin_support, x.pathway_qvalue |
| FROM '{base}/pd_tree.parquet' t |
| JOIN '{base}/pd_toxin_pathway.parquet' x USING (pathway_index) |
| WHERE t.drug_relation = 'target' AND x.pathway_qvalue < 0.05 |
| ORDER BY x.toxin_support DESC, t.drug_name |
| """).fetchall() |
| ``` |
| |
| ## Source and license |
| |
| Derived from **PrimeKG** by Chandak, Huang and Zitnik. Note that PrimeKG's MIT license covers |
| the software; the upstream repository states that this is distinct from the terms governing |
| the data itself, which is distributed via Harvard Dataverse |
| ([doi:10.7910/DVN/IXA7BM](https://doi.org/10.7910/DVN/IXA7BM)) and inherits the terms of its |
| own ~20 constituent primary sources. Check those before redistributing. The `license` field in |
| this repo's metadata (CC-BY-4.0) describes the GenomeScreen tables above. |
| |
| The toxin overlay adds a second source: the toxin list and its gene targets come from |
| **PubChem** (compound records and their literature-co-mentioned genes) and PubMed mention |
| counts, retrieved via the PubChem PUG-REST API. PubChem aggregates from many depositors whose |
| individual terms vary; see https://www.ncbi.nlm.nih.gov/home/about/policies/. The EC-to-gene |
| map and the specificity weighting are original to this dataset. |
| |
| > Chandak, P., Huang, K., Zitnik, M. *Building a knowledge graph to enable precision medicine.* |
| > Nature Scientific Data 10, 67 (2023). |
| |
| ```bibtex |
| @article{Chandak2023PrimeKG, |
| title={Building a knowledge graph to enable precision medicine}, |
| author={Chandak, Payal and Huang, Kexin and Zitnik, Marinka}, |
| journal={Scientific Data}, |
| volume={10}, |
| number={1}, |
| pages={67}, |
| year={2023}, |
| doi={10.1038/s41597-023-01960-3} |
| } |
| ``` |
| |
