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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.


Closed discovery process: the process starts simultaneously from A and C resulting in overlapping Bs.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig2: Closed discovery process: the process starts simultaneously from A and C resulting in overlapping Bs.

Mentions: Closed Discovery. A closed discovery process is the testing of a hypothesis. If the researcher has already formed a hypothesis, possibly by the open discovery route described above, he (or she) can elaborate and test it from the literature. Figure 2 depicts the approach: starting from both disease A and substance C, the researcher tries to find common intermediate B terms.


Supervised Learning Based Hypothesis Generation from Biomedical Literature.

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

Closed discovery process: the process starts simultaneously from A and C resulting in overlapping Bs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Closed discovery process: the process starts simultaneously from A and C resulting in overlapping Bs.
Mentions: Closed Discovery. A closed discovery process is the testing of a hypothesis. If the researcher has already formed a hypothesis, possibly by the open discovery route described above, he (or she) can elaborate and test it from the literature. Figure 2 depicts the approach: starting from both disease A and substance C, the researcher tries to find common intermediate B terms.

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.