Supervised Learning Based Hypothesis Generation from Biomedical Literature

• Published in: BioMed research international Vol. 2015; no. 2015; pp. 1 - 12
• Main Authors: Lin, Hongfei, Li, Zongyao, Yang, Zhihao, Sang, Shengtian
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015; John Wiley & Sons, Inc
• Subjects: Activity based costing; Algorithms; Data Mining; Educational aspects; Fish oils; Humans; Information retrieval; Learning strategies; Medical education; Medical Informatics Computing; Medical literature; Medical research; Medical Subject Headings-MeSH; Methods; Natural Language Processing; Physiology; Raynaud disease; Semantics; Statistical analysis
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2015/698527

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.