Format

Send to

Choose Destination
BMC Bioinformatics. 2016 Jan 11;17 Suppl 1:1. doi: 10.1186/s12859-015-0844-1.

Weakly supervised learning of biomedical information extraction from curated data.

Author information

1
Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. suj011@eng.ucsd.edu.
2
Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. ktumkur@eng.ucsd.edu.
3
Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. tskuo@ucsd.edu.
4
Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. shbharga@eng.ucsd.edu.
5
Department of Computer Science and Engineering, Jacobs School of Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. golin@eng.ucsd.edu.
6
Department of Biomedical Informatics, School of Medicine, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, USA. chunnan@ucsd.edu.

Abstract

BACKGROUND:

Numerous publicly available biomedical databases derive data by curating from literatures. The curated data can be useful as training examples for information extraction, but curated data usually lack the exact mentions and their locations in the text required for supervised machine learning. This paper describes a general approach to information extraction using curated data as training examples. The idea is to formulate the problem as cost-sensitive learning from noisy labels, where the cost is estimated by a committee of weak classifiers that consider both curated data and the text.

RESULTS:

We test the idea on two information extraction tasks of Genome-Wide Association Studies (GWAS). The first task is to extract target phenotypes (diseases or traits) of a study and the second is to extract ethnicity backgrounds of study subjects for different stages (initial or replication). Experimental results show that our approach can achieve 87% of Precision-at-2 (P@2) for disease/trait extraction, and 0.83 of F1-Score for stage-ethnicity extraction, both outperforming their cost-insensitive baseline counterparts.

CONCLUSIONS:

The results show that curated biomedical databases can potentially be reused as training examples to train information extractors without expert annotation or refinement, opening an unprecedented opportunity of using "big data" in biomedical text mining.

PMID:
26817711
PMCID:
PMC4847485
DOI:
10.1186/s12859-015-0844-1
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center