open_targets.association_by_datasource_indirect
import bedrock_bio as bb
rel = bb.load_table('open_targets.association_by_datasource_indirect')
df = rel.limit(5).pl() # Polars
df = rel.limit(5).df() # Pandas
df.head()| diseaseId | targetId | aggregationType | aggregationValue | associationScore | evidenceCount | timeseries | currentNovelty | version | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | EFO_0000180 | ENSG00000086065 | datasourceId | europepmc | 0.076037 | 2 | [{'year': 2019, 'associationScore': 0.0, 'nove... | 0.066973 | 26.09 |
| 1 | EFO_0000180 | ENSG00000091513 | datasourceId | europepmc | 0.079417 | 16 | [{'year': 2002, 'associationScore': 0.01824890... | 0.000825 | 26.09 |
| 2 | EFO_0000180 | ENSG00000121892 | datasourceId | europepmc | 0.060830 | 1 | [{'year': 2015, 'associationScore': 0.06082967... | 0.000000 | 26.09 |
| 3 | EFO_0000180 | ENSG00000136381 | datasourceId | europepmc | 0.012166 | 1 | [{'year': 2019, 'associationScore': 0.0, 'nove... | 0.001450 | 26.09 |
| 4 | EFO_0000180 | ENSG00000160791 | datasourceId | gwas_credible_sets | 0.030736 | 1 | [{'year': 1981, 'associationScore': 0.0, 'nove... | 0.000000 | 26.09 |
library(bedrockbio)
lf <- load_table("open_targets.association_by_datasource_indirect")
df <- head(lf, 5) |> collect()
head(df)Sample output (5 rows, showing 8 of 9 columns) diseaseId | targetId | aggregationType | aggregationValue | associationScore | evidenceCount | currentNovelty | version ----------- | --------------- | --------------- | ------------------ | -------------------- | ------------- | --------------------- | ------- 0 EFO_0000180 | ENSG00000086065 | datasourceId | europepmc | 0.07603708963448856 | 2 | 0.06697324636799985 | 26.09 1 EFO_0000180 | ENSG00000091513 | datasourceId | europepmc | 0.07941729120419239 | 16 | 0.000825362578465404 | 26.09 2 EFO_0000180 | ENSG00000121892 | datasourceId | europepmc | 0.06082967170759085 | 1 | 0 | 26.09 3 EFO_0000180 | ENSG00000136381 | datasourceId | europepmc | 0.01216593434151817 | 1 | 0.0014502149226381924 | 26.09 4 EFO_0000180 | ENSG00000160791 | datasourceId | gwas_credible_sets | 0.030736350267207304 | 1 | 0 | 26.09
Description
Section titled “Description”Data source-specific metrics for indirect target-disease associations and its development over time implying association novelty.
Dataset contains association scores calculated for each data source indepedently for the aggregation of all evidence supporting any given target-disease.
This dataset contains indirect associations, therefore evidence is propagated across the disease or phenotype space using the Experimental Factor Ontology [Target-Disease]
Partitions
Section titled “Partitions”Not partitioned.
Schema
Section titled “Schema”| Column | Type | Description |
|---|---|---|
diseaseId |
TEXT |
Open Targets disease identifier |
targetId |
TEXT |
Unique identifier for the target [bioregistry:ensembl] |
aggregationType |
TEXT |
Evidence column used to aggregate data: datasourceId |
aggregationValue |
TEXT |
Datasource identifier of the group: eg. gwas_credible_set |
associationScore |
DOUBLE |
Current disease/target association score of evidence from the given datasource |
evidenceCount |
BIGINT |
Total number of evidence supporting the disease/target association for a given datasource |
timeseries |
STRUCT(year INT, associationScore DOUBLE, novelty DOUBLE, yearlyEvidenceCount INT)[] |
Timeseries describing the evolution of association |
currentNovelty |
DOUBLE |
Novelty of the disease/target association |
version |
TEXT |
Open Targets Platform release version (major.minor), e.g. 26.09. Added during ingest. |