66 · SemaTyP

Drug-Disease Association Knowledge Graph from literature mining + TTD Category: Drug-centric | Type: KG | Subcategory: Drug-Disease Associations Access: Local files (downloaded from GitHub)

Resource	URL
GitHub	https://github.com/ShengtianSang/SemaTyP
Paper	https://link.springer.com/article/10.1186/s12859-018-2167-5

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What it provides

SemaTyP combines two data sources into a knowledge graph for drug discovery / repositioning:

• SemMedDB predications (data/SemmedDB/predications.txt): subject–predicate–object triples with UMLS semantic types, extracted from PubMed abstracts (full version: ~39M triples).
• TTD curated associations (data/TTD/): drug-disease and target-disease links from Therapeutic Target Database (2016), with ICD-9/ICD-10 codes.
• Processed associations (data/processed/): pre-computed drug→disease, disease→drug, disease→target mappings.

Data schema

File	Format	Columns
data/SemmedDB/predications.txt	TSV	subject, object, context, predicate, subj_semtype, obj_semtype
data/TTD/drug-disease_TTD2016.txt	TSV	TTDDRUGID, drug_name, indication, ICD9, ICD10
data/TTD/target-disease_TTD2016.txt	TSV	target_id, target_name, disease, ICD9, ICD10
data/processed/drug_disease	TSV	drug, disease, ...
data/processed/disease_drug	TSV	disease, drug, ...
data/processed/disease_targets	TSV	disease, target, ...

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Setup

Data must be downloaded locally first:

git clone https://github.com/ShengtianSang/SemaTyP.git

Then set the data path (default points to your HiPerGator location):

# Option 1: environment variable
export SEMATYP_DATA_DIR="/path/to/SemaTyP-main"

# Option 2: edit DATA_DIR in 66_SemaTyP.py directly

Note: The GitHub repo only contains a 100-line sample of predications.txt. The full 39M-triple file must be downloaded separately per the repo README.

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Quick start

from 66_SemaTyP import query

# Single entity (drug, disease, target, or any biomedical concept)
results = query("aspirin")

# Multiple entities
results = query(["metformin", "diabetes", "CYP2D6"])

# Specific data source only
results = query("imatinib", fields="predications", pred_limit=10)
results = query("Schizophrenia", fields="ttd")
results = query("cancer", fields="processed")

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query() interface

query(entities, fields="all", pred_limit=50) -> list[dict]

Parameter	Type	Description
entities	str | list[str]	Entity name(s), case-insensitive
fields	str	"all" — everything; "predications" / "ttd" / "processed"
pred_limit	int	Max predication triples per entity (default 50)

Return structure (fields="all")

[
  {
    "query": "aspirin",
    "predications": [
      {"subject": "aspirin", "object": "pain", "predicate": "TREATS",
       "context": "...", "subj_type": "phsu", "obj_type": "sosy"}
    ],
    "predication_count": 42,
    "ttd_drug_disease": [
      {"ttdid": "DAP000XXX", "drug": "Aspirin", "disease": "Pain",
       "icd9": "...", "icd10": "..."}
    ],
    "ttd_target_disease": [],
    "processed_drug_disease": [...],
    "processed_disease_drug": [...],
    "processed_disease_targets": [...]
  }
]

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Lower-level functions

Function	Input	Output	Description
get_predications(entity, limit=50)	entity name	list[dict]	SemMedDB KG triples
get_ttd_drug_disease(entity)	drug or disease	list[dict]	TTD drug-disease associations
get_ttd_target_disease(entity)	target or disease	list[dict]	TTD target-disease associations
get_processed_drug_disease(entity)	entity name	list[dict]	Processed drug→disease
get_processed_disease_drug(entity)	entity name	list[dict]	Processed disease→drug
get_processed_disease_targets(entity)	entity name	list[dict]	Processed disease→target

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Notes

• All lookups are case-insensitive (indexed by lowercased entity names).
• Data is lazy-loaded: first query() call triggers a one-time index build (may take seconds for large predications file).
• UMLS semantic type codes in predications: phsu = pharmaceutical substance, dsyn = disease/syndrome, gngm = gene/genome, sosy = sign/symptom, podg = patient/group, etc.
• The GitHub sample predications.txt has only 100 lines; for full coverage, download the complete file per the repo README instructions.