open_targets.association_overall_indirect
import bedrock_bio as bb
rel = bb.load_table('open_targets.association_overall_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 | ENSG00000009413 | overall | None | 0.001480 | 1 | [{'year': 2023, 'associationScore': 0.00148009... | 1.764322e-04 | 26.09 |
| 1 | EFO_0000180 | ENSG00000042832 | overall | None | 0.009991 | 2 | [{'year': 1998, 'associationScore': 0.00444029... | 0.000000e+00 | 26.09 |
| 2 | EFO_0000180 | ENSG00000082175 | overall | None | 0.038020 | 3 | [{'year': 2009, 'associationScore': 0.03700248... | 3.102197e-08 | 26.09 |
| 3 | EFO_0000180 | ENSG00000141522 | overall | None | 0.007030 | 2 | [{'year': 2011, 'associationScore': 0.0, 'nove... | 3.633696e-04 | 26.09 |
| 4 | EFO_0000180 | ENSG00000149591 | overall | None | 0.001480 | 1 | [{'year': 2025, 'associationScore': 0.00148009... | 1.764322e-04 | 26.09 |
library(bedrockbio)
lf <- load_table("open_targets.association_overall_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 | ENSG00000009413 | overall | None | 0.0014800995840213117 | 1 | 0.00017643219529906104 | 26.09 1 EFO_0000180 | ENSG00000042832 | overall | None | 0.009990672192143854 | 2 | 0 | 26.09 2 EFO_0000180 | ENSG00000082175 | overall | None | 0.03802005806454743 | 3 | 3.102197428780511e-8 | 26.09 3 EFO_0000180 | ENSG00000141522 | overall | None | 0.00703047302410123 | 2 | 0.00036336955053457604 | 26.09 4 EFO_0000180 | ENSG00000149591 | overall | None | 0.0014800995840213117 | 1 | 0.00017643219529906104 | 26.09
Description
Section titled “Description”Overall metrics for indirect target-disease associations and its development over time implying association novelty.
Dataset contains the overall association score 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 |
How evidence data was aggregated: overall |
aggregationValue |
TEXT |
For overall association this column is empty |
associationScore |
DOUBLE |
Current disease/target association score of evidence from any datasource |
evidenceCount |
BIGINT |
Total number of evidence supporting the disease/target association for any datasource |
timeseries |
STRUCT(year INT, associationScore DOUBLE, novelty DOUBLE, yearlyEvidenceCount BIGINT)[] |
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. |