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BioMed Research International
Volume 2015, Article ID 698527, 12 pages
http://dx.doi.org/10.1155/2015/698527
Research Article

Supervised Learning Based Hypothesis Generation from Biomedical Literature

College of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, China

Received 16 January 2015; Revised 12 April 2015; Accepted 24 May 2015

Academic Editor: Hung-Yu Kao

Copyright © 2015 Shengtian Sang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nowadays, the amount of biomedical literatures is growing at an explosive speed, and there is much useful knowledge undiscovered in this literature. Researchers can form biomedical hypotheses through mining these works. In this paper, we propose a supervised learning based approach to generate hypotheses from biomedical literature. This approach splits the traditional processing of hypothesis generation with classic ABC model into AB model and BC model which are constructed with supervised learning method. Compared with the concept cooccurrence and grammar engineering-based approaches like SemRep, machine learning based models usually can achieve better performance in information extraction (IE) from texts. Then through combining the two models, the approach reconstructs the ABC model and generates biomedical hypotheses from literature. The experimental results on the three classic Swanson hypotheses show that our approach outperforms SemRep system.