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BioEve Search: A Novel Framework to Facilitate Interactive Literature Search.

Ahmed ST, Davulcu H, Tikves S, Nair R, Zhao Z - Adv Bioinformatics (2012)

Bottom Line: It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking.Conclusions.The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.

ABSTRACT
Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named "BioEve") that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease.

No MeSH data available.


“Hepatic-lipase” selected.
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Related In: Results  -  Collection

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fig4: “Hepatic-lipase” selected.

Mentions: (2) In left panel, “hepatic lipase” is highlighted; let us click on that as it shows some important relationship between “cholesterol” and “hepatic lipase.” The search results are now narrowed down to 195 articles from 27177 (Figure 4). That is still a lot of articles to read this afternoon, how about some insights on diseases.


BioEve Search: A Novel Framework to Facilitate Interactive Literature Search.

Ahmed ST, Davulcu H, Tikves S, Nair R, Zhao Z - Adv Bioinformatics (2012)

“Hepatic-lipase” selected.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368157&req=5

fig4: “Hepatic-lipase” selected.
Mentions: (2) In left panel, “hepatic lipase” is highlighted; let us click on that as it shows some important relationship between “cholesterol” and “hepatic lipase.” The search results are now narrowed down to 195 articles from 27177 (Figure 4). That is still a lot of articles to read this afternoon, how about some insights on diseases.

Bottom Line: It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking.Conclusions.The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA.

ABSTRACT
Background. Recent advances in computational and biological methods in last two decades have remarkably changed the scale of biomedical research and with it began the unprecedented growth in both the production of biomedical data and amount of published literature discussing it. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also pave the way to discover hitherto unknown information implicitly conveyed in the texts. Results. We developed a novel framework (named "BioEve") that seamlessly integrates Faceted Search (Information Retrieval) with Information Extraction module to provide an interactive search experience for the researchers in life sciences. It enables guided step-by-step search query refinement, by suggesting concepts and entities (like genes, drugs, and diseases) to quickly filter and modify search direction, and thereby facilitating an enriched paradigm where user can discover related concepts and keywords to search while information seeking. Conclusions. The BioEve Search framework makes it easier to enable scalable interactive search over large collection of textual articles and to discover knowledge hidden in thousands of biomedical literature articles with ease.

No MeSH data available.