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Supervised Learning Based Hypothesis Generation from Biomedical Literature.

Sang S, Yang Z, Li Z, Lin H - Biomed Res Int (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.


The results of training AB and BC models of every learning round by cotraining algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4561867&req=5

fig5: The results of training AB and BC models of every learning round by cotraining algorithms.

Mentions: The best testing result is from the second learning round (the classifier is M12 (graph kernel) and the training set is T12): an F-score of 77.8% is achieved as shown in Figure 5. After five rounds of cotraining learning, although there are fluctuations, the F-score of feature-based kernel achieves a continuous improvement and finally reaches 76.82%.


Supervised Learning Based Hypothesis Generation from Biomedical Literature.

Sang S, Yang Z, Li Z, Lin H - Biomed Res Int (2015)

The results of training AB and BC models of every learning round by cotraining algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4561867&req=5

fig5: The results of training AB and BC models of every learning round by cotraining algorithms.
Mentions: The best testing result is from the second learning round (the classifier is M12 (graph kernel) and the training set is T12): an F-score of 77.8% is achieved as shown in Figure 5. After five rounds of cotraining learning, although there are fluctuations, the F-score of feature-based kernel achieves a continuous improvement and finally reaches 76.82%.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

No MeSH data available.