Weakly supervised learning of biomedical information extraction from curated data.
Weakly supervised learning of biomedical information extraction from curated data.
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DOI：
10.1186/s12859-015-0844-1
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发表时间：
2016-01-11
影响因子：
3
通讯作者：
Hsu CN
中科院分区：
文献类型：
--
作者：
Jain S;Tumkur KR;Kuo TT;Bhargava S;Lin G;Hsu CN
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. 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. 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. The online version of this article (doi:10.1186/s12859-015-0844-1) contains supplementary material, which is available to authorized users.
