open_targets.literature_vector

Python

In [1]:

import bedrock_bio as bb

rel = bb.load_table('open_targets.literature_vector')

df = rel.limit(5).pl()  # Polars
df = rel.limit(5).df()  # Pandas
df.head()

Out[1]:

	category	word	norm	vector	version
0	drug	CHEMBL3976807	0.456121	[-0.069809, -0.043121, -0.032723, -0.066152, -...	26.09
1	target	ENSG00000172410	0.912461	[-0.17844, 0.005906, -0.076802, -0.007208, -0....	26.09
2	disease	MONDO_0017258	0.659421	[-0.011891, -0.236128, -0.075477, -0.023771, -...	26.09
3	drug	CHEMBL2106600	0.088838	[0.000224, 0.013033, -0.003227, -0.000544, 0.0...	26.09
4	disease	HP_0001998	1.851000	[-0.060543, -0.12331, -0.242327, -0.203655, 0....	26.09

R

In [1]:

library(bedrockbio)

lf <- load_table("open_targets.literature_vector")
df <- head(lf, 5) |> collect()

head(df)

Out[1]:

Sample output (5 rows, showing 4 of 5 columns)
   category | word            | norm                | version
   -------- | --------------- | ------------------- | -------
0  drug     | CHEMBL3976807   | 0.4561208016532358  | 26.09
1  target   | ENSG00000172410 | 0.9124607639347971  | 26.09
2  disease  | MONDO_0017258   | 0.6594205890752463  | 26.09
3  drug     | CHEMBL2106600   | 0.08883784864857247 | 26.09
4  disease  | HP_0001998      | 1.8510004868582992  | 26.09

Description

Word to Vector representation of entity matches in literature [Literature]

Partitions

Not partitioned.

Schema

Column	Type	Description
category	TEXT	Category of the entity match in the literature. Either target, drug or disease
word	TEXT	Normalised identifier of the matched entity
norm	DOUBLE	Normalised form of the extracted word
vector	DOUBLE[]	Vector representation of the word based on word embedding
version	TEXT	Open Targets Platform release version (major.minor), e.g. 26.09. Added during ingest.