Supervised Learning Based Hypothesis Generation from Biomedical Literature
Joint Authors
Yang, Zhihao
Lin, Hongfei
Sang, Shengtian
Li, Zongyao
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-08-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sang, Shengtian& Yang, Zhihao& Li, Zongyao& Lin, Hongfei. 2015. Supervised Learning Based Hypothesis Generation from Biomedical Literature. BioMed Research International،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1056427
Modern Language Association (MLA)
Sang, Shengtian…[et al.]. Supervised Learning Based Hypothesis Generation from Biomedical Literature. BioMed Research International No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1056427
American Medical Association (AMA)
Sang, Shengtian& Yang, Zhihao& Li, Zongyao& Lin, Hongfei. Supervised Learning Based Hypothesis Generation from Biomedical Literature. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1056427
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1056427