Limits...
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 result of closed discovery of “Raynaud's disease and fish oils.” The purple nodes in the figure are the linking terms discovered only by our method; the yellow nodes represent the linking terms found only by SemRep Database; and the blue node represents the terms found by both methods.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4561867&req=5

fig6: The result of closed discovery of “Raynaud's disease and fish oils.” The purple nodes in the figure are the linking terms discovered only by our method; the yellow nodes represent the linking terms found only by SemRep Database; and the blue node represents the terms found by both methods.

Mentions: The only difference between the SemRep method and our method is that its effective linking terms are obtained by taking the intersection of two sets of linking terms (the two sets of linking terms are all retrieved from SemRep instead of using the AB model and BC model, and one set is retrieved with the initial terms and the other is retrieved with the target terms). Figure 6 shows the result obtained by the two methods. In addition, the other two classic Swanson's hypotheses migraine and magnesium and Alzheimer's disease and indomethacin are also verified with our method and the results are shown in Table 5, and part of the effective linking terms discovered by our method are shown in Table 6.


Supervised Learning Based Hypothesis Generation from Biomedical Literature.

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

The result of closed discovery of “Raynaud's disease and fish oils.” The purple nodes in the figure are the linking terms discovered only by our method; the yellow nodes represent the linking terms found only by SemRep Database; and the blue node represents the terms found by both methods.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: The result of closed discovery of “Raynaud's disease and fish oils.” The purple nodes in the figure are the linking terms discovered only by our method; the yellow nodes represent the linking terms found only by SemRep Database; and the blue node represents the terms found by both methods.
Mentions: The only difference between the SemRep method and our method is that its effective linking terms are obtained by taking the intersection of two sets of linking terms (the two sets of linking terms are all retrieved from SemRep instead of using the AB model and BC model, and one set is retrieved with the initial terms and the other is retrieved with the target terms). Figure 6 shows the result obtained by the two methods. In addition, the other two classic Swanson's hypotheses migraine and magnesium and Alzheimer's disease and indomethacin are also verified with our method and the results are shown in Table 5, and part of the effective linking terms discovered by our method are shown in Table 6.

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.