Continuous Evaluation of Relational Learning in Biomedicine (CERLIB)

KG-COVID-19

Introduction

KG-COVID-19 is a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

Relation types

The challenge we suggest is prediction of drug targets, which are represented in the COVID-19 knowledge graph as:

drug (biolink:Drug) 

-- biolink:molecularly_interacts_with || biolink:interacts_with → 

target (biolink:Protein | biolink:Gene)

of which there are currently 3372 (see query below)

Data sources and contact

Github: https://github.com/Knowledge-Graph-Hub/kg-covid-19
Contact and discussion: https://github.com/Knowledge-Graph-Hub/kg-covid-19/pull/388/files

Data download and evaluation

Dataset download: https://kg-hub.berkeleybop.io/kg-covid-19/current/kg-covid-19.nt.gz

Endpoint: http://10.254.146.165:8891/sparql

Query for evaluation:

prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
prefix bl: <https://w3id.org/biolink/vocab/>
prefix xsd: <http://www.w3.org/2001/XMLSchema#>

SELECT ?drug ?pred ?target WHERE
{
  ?drug ?pred ?target .
  ?drug bl:category ?drugcat .
  ?target bl:category ?targetcat; <https://www.example.org/UNKNOWN/ncbi_taxid> "9606"^^xsd:string .
  OPTIONAL { ?drug rdfs:label ?druglab } .
  OPTIONAL { ?target rdfs:label ?targetlab } .
  FILTER ( ?drugcat = bl:Drug || ?drugcat = bl:ChemicalSubstance )
  FILTER ( ?targetcat = bl:Protein || ?targetcat = bl:Gene )
  FILTER ( ?pred = bl:molecularly_interacts_with || ?pred = bl:interacts_with )
}

Rankings

Ranking

New submission

To submit, create a compressed (gzip or zip) tab-separated file containing (subject, predicate, object, score) quadruples. An example file could be:

<https://reactome.org/content/detail/R-HSA-2172127> <http://purl.obolibrary.org/obo/RO_0000057> <http://www.ncbi.nlm.nih.gov/gene/5710> 0.9
<http://purl.obolibrary.org/obo/CHEBI_4034> <http://purl.obolibrary.org/obo/RO_0002434> <https://uswest.ensembl.org/Homo_sapiens/Transcript/Summary?t=ENST00000546491> 0.1
<http://purl.obolibrary.org/obo/CHEBI_49662> <http://purl.obolibrary.org/obo/RO_0002434> <https://uswest.ensembl.org/Homo_sapiens/Transcript/Summary?t=ENST00000344604> 0.231
<http://purl.obolibrary.org/obo/PR_P63010> <http://purl.obolibrary.org/obo/RO_0002436> <http://purl.obolibrary.org/obo/PR_P51812> 0.111
<http://purl.obolibrary.org/obo/MONDO_0009452> <http://purl.obolibrary.org/obo/RO_0002200> <http://purl.obolibrary.org/obo/HP_0001290> 0
<http://purl.obolibrary.org/obo/CHEBI_46195> <http://purl.obolibrary.org/obo/RO_0002434> <https://uswest.ensembl.org/Homo_sapiens/Transcript/Summary?t=ENST00000546299> 1.0
<http://purl.obolibrary.org/obo/PR_Q8ND25> <http://purl.obolibrary.org/obo/RO_0002512> <https://uswest.ensembl.org/Homo_sapiens/Transcript/Summary?t=ENST00000320619> 0.4291
<http://www.ncbi.nlm.nih.gov/gene/2065> <http://purl.obolibrary.org/obo/RO_0002559> <https://www.ncbi.nlm.nih.gov/snp/rs115311663> 0.1
<http://purl.obolibrary.org/obo/PR_Q13480-2> <http://purl.obolibrary.org/obo/RO_0002436> <http://purl.obolibrary.org/obo/PR_P16333-1> 0.5
<http://purl.obolibrary.org/obo/CHEBI_78802> <http://purl.obolibrary.org/obo/RO_0002436> <http://purl.obolibrary.org/obo/GO_0006875> 0

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