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Disease Related Knowledge Summarization Based on Deep Graph Search.

Wu X, Yang Z, Li Z, Lin H, Wang J - Biomed Res Int (2015)

Bottom Line: Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher's information needs.In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information.Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities.

View Article: PubMed Central - PubMed

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

ABSTRACT
The volume of published biomedical literature on disease related knowledge is expanding rapidly. Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher's information needs. In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction for Carcinoma of bladder and outperforms the method of Combo.

No MeSH data available.


Related in: MedlinePlus

Top 10 predications scored by random walk with depth 4.
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Related In: Results  -  Collection


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figbox1: Top 10 predications scored by random walk with depth 4.

Mentions: Our scoring method ranks the entities based on the stationary distribution of the random walk described above. We obtain 240 semantic predications for depth 4 and 4,679 semantic predications for depth 6 by the random walk filtering. Top 10 predications of depth 4 are shown in Box 1. For example, we analyze the returned relations Carcinoma of bladder→AFFECTS→Smoker→PREDISPOSES→Chromosomal Instability and Carcinoma of bladder→AFFECTS→Dysplasia→COEXISTS_WITH→HRAS gene in Box 1 using PubMed as the reference. There are 29 records returned about Carcinoma of bladder and Smoker and 415 records returned for Dysplasia and Carcinoma of bladder.


Disease Related Knowledge Summarization Based on Deep Graph Search.

Wu X, Yang Z, Li Z, Lin H, Wang J - Biomed Res Int (2015)

Top 10 predications scored by random walk with depth 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4561941&req=5

figbox1: Top 10 predications scored by random walk with depth 4.
Mentions: Our scoring method ranks the entities based on the stationary distribution of the random walk described above. We obtain 240 semantic predications for depth 4 and 4,679 semantic predications for depth 6 by the random walk filtering. Top 10 predications of depth 4 are shown in Box 1. For example, we analyze the returned relations Carcinoma of bladder→AFFECTS→Smoker→PREDISPOSES→Chromosomal Instability and Carcinoma of bladder→AFFECTS→Dysplasia→COEXISTS_WITH→HRAS gene in Box 1 using PubMed as the reference. There are 29 records returned about Carcinoma of bladder and Smoker and 415 records returned for Dysplasia and Carcinoma of bladder.

Bottom Line: Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher's information needs.In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information.Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities.

View Article: PubMed Central - PubMed

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

ABSTRACT
The volume of published biomedical literature on disease related knowledge is expanding rapidly. Traditional information retrieval (IR) techniques, when applied to large databases such as PubMed, often return large, unmanageable lists of citations that do not fulfill the searcher's information needs. In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, firstly Kullback-Leibler Divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden (indirect) relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction for Carcinoma of bladder and outperforms the method of Combo.

No MeSH data available.


Related in: MedlinePlus