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 procedure of open discovery with our method.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4561867&req=5

fig8: The procedure of open discovery with our method.

Mentions: The processes of open discovery with different methods are shown in Figures 7 and 8, respectively. In the process of open discovery by using SemRep Database (Figure 7), first the initial terms (A terms) are specified by providing concept “Migraine Disorder,” which is used as a keyword to retrieve data from the SemRep Database. Then we obtain all the sentences which contain both initial terms (Migraine Disorder) and linking terms (other entities). Second, we filter all the sentences obtained from step one, and the specific rules are the same as we mentioned in closed discovery (we filter out the sentences which do not contain any concept belonging to semantic type list or contain the concepts in broad concept list). The third step is similar to the first step, all the linking terms (extracted from every sentence from the filter step) are used as keywords to retrieve data from the SemRep Database; then we get all the sentences which contain both linking terms (B terms) and target terms (C terms). At last we rank all the target terms and output the results. The scoring rules are applied to rank the target terms [24].


Supervised Learning Based Hypothesis Generation from Biomedical Literature.

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

The procedure of open discovery with our method.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: The procedure of open discovery with our method.
Mentions: The processes of open discovery with different methods are shown in Figures 7 and 8, respectively. In the process of open discovery by using SemRep Database (Figure 7), first the initial terms (A terms) are specified by providing concept “Migraine Disorder,” which is used as a keyword to retrieve data from the SemRep Database. Then we obtain all the sentences which contain both initial terms (Migraine Disorder) and linking terms (other entities). Second, we filter all the sentences obtained from step one, and the specific rules are the same as we mentioned in closed discovery (we filter out the sentences which do not contain any concept belonging to semantic type list or contain the concepts in broad concept list). The third step is similar to the first step, all the linking terms (extracted from every sentence from the filter step) are used as keywords to retrieve data from the SemRep Database; then we get all the sentences which contain both linking terms (B terms) and target terms (C terms). At last we rank all the target terms and output the results. The scoring rules are applied to rank the target terms [24].

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.