Empirical study using network of semantically related associations in bridging the knowledge gap

Journal of Translational Medicine

Ramin Zand²  Mohammed Yeasin¹  Vida Abedi³ 

[1] College of Arts and Sciences, Bioinformatics Program, Memphis University, Memphis 38152, TN, USA;Department of Neurology, University of Tennessee Health Science Center, Memphis 38163, TN, USA;The Center for Modeling Immunity to Entering Pathogens, Nutritional Immunology and Molecular Medicine Laboratory, Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg 24060, VA, USA

关键词: Semantic associations;    Network of association;    Latent semantic analysis (LSA);    Multi-gram dictionary;    Medical subject headings (MeSH);    PubMed;    Ontology mapping;    Literature mining;    Hypothesis generation;    Knowledge discovery;   

Others  :  1147066
DOI  :  10.1186/s12967-014-0324-9

 received in 2014-08-22, accepted in 2014-11-11,  发布年份 2014

【 摘 要 】

Background

The data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge.

Methods

In this paper, we highlight some of the findings using a text analytics tool, called ARIANA - Adaptive Robust and Integrative Analysis for finding Novel Associations.

Results

Empirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model.

Conclusion

An integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.

【 授权许可】

   
2014 Abedi et al.; licensee BioMed Central Ltd.

【 图 表 】

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