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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

P@N of KM and Combo. P@N is the precision of top N samples in the ranking. N is the number of samples.
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Related In: Results  -  Collection


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fig3: P@N of KM and Combo. P@N is the precision of top N samples in the ranking. N is the number of samples.

Mentions: KM returned 84 results, among which 50 genes are related to Carcinoma of bladder in PubMed and regarded as true positive (TP). The precision of KM is 60%. In 95 results returned by Combo, 49 genes are true positive (TP), and the precision of Combo is 53%. The KM method achieves a better precision than the Combo method. In addition, as shown in Figure 3, the P@N scores of KM are much higher than those of Combo. All these show that the precision performance of KM is better than that of Combo.


Disease Related Knowledge Summarization Based on Deep Graph Search.

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

P@N of KM and Combo. P@N is the precision of top N samples in the ranking. N is the number of samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: P@N of KM and Combo. P@N is the precision of top N samples in the ranking. N is the number of samples.
Mentions: KM returned 84 results, among which 50 genes are related to Carcinoma of bladder in PubMed and regarded as true positive (TP). The precision of KM is 60%. In 95 results returned by Combo, 49 genes are true positive (TP), and the precision of Combo is 53%. The KM method achieves a better precision than the Combo method. In addition, as shown in Figure 3, the P@N scores of KM are much higher than those of Combo. All these show that the precision performance of KM is better than that of Combo.

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