Table 2 Accuracy evaluation of literature mined results in Cancer-Alterome.

From: Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer

Named entity recognition and normalization

Evaluation metric

Count of prediction

Tools

Entity type

Precision

Recall

F1 Score

Mention

Entity

Normalization

PubTator20

Gene

0.79

0.81

0.80

203,939

22,021

Entrez ID

PubTator20

Point mutations and SNPs

0.81

0.81

0.83

197,595

164,144

rsID

AGAC-NER24

General genetic alteration

0.74

0.57

0.64

3,002,082

549

Dictionary

AGAC-NER24

Trigger word

0.78

0.70

0.74

200,670

—

—

OGER++22

GO

0.72

0.17

0.27

52,140

22,874

GO ID

PhenoTagger23

HPO

0.79

0.70

0.74

24,261

8,345

HPO ID

PubTator20

MeSH

0.83

0.82

0.81

1,131,933

45,119

MeSH ID

Relation extraction

Evaluation metric

Count of prediction

Tools

Relation type

Precision

Recall

F1 Score

Relations

AGAC-RE24

Theme

0.87

0.84

0.91

37,411,752

AGAC-RE24

Cause

0.88

0.85

0.82

12,420,489

Regulatory events identification

Evaluation metric

Count of prediction

Method

Event type

Precision

Recall

F1 Score

Events

Template match

GARE

0.84

0.96

0.90

16,681,473

  1. (a) Evaluation of NLP tools for named entity recognition and normalization. (b) Evaluation of NLP tools for relation extraction. (c) Evaluation of regulatory events (GAREs).