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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 process of training AB model.
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Related In: Results  -  Collection


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fig4: The process of training AB model.

Mentions: As shown in Figure 4, the process of training AB model is as follows: at first, two initial SVM classifiers, M10 (graph-based kernel) and M20 (feature-based kernel), are trained using initial training set Tinitial which contains 500 labeled examples, and the test results of two classifiers are as follows: with classifier M10, the values of precision, recall, and F-score are 72.88%, 83.33%, and 77.76%, respectively. And at the same time, the results of classifier M20 are 74.35%, 76.33%, and 75.33%, respectively.


Supervised Learning Based Hypothesis Generation from Biomedical Literature.

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

The process of training AB model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: The process of training AB model.
Mentions: As shown in Figure 4, the process of training AB model is as follows: at first, two initial SVM classifiers, M10 (graph-based kernel) and M20 (feature-based kernel), are trained using initial training set Tinitial which contains 500 labeled examples, and the test results of two classifiers are as follows: with classifier M10, the values of precision, recall, and F-score are 72.88%, 83.33%, and 77.76%, respectively. And at the same time, the results of classifier M20 are 74.35%, 76.33%, and 75.33%, respectively.

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.