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Mining Biomedical Data for Hidden Relationship Discovery

With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked. Literature based discovery (LBD) is in practice for many years to identify plausible associations between previously unrelated concepts. In this paper, we present a new, completely automatic and interactive system that creates a graph-based knowledge base to capture multifaceted complex associations among biomedical concepts. For a given pair of input concepts, our system auto-generates a list of ranked subgraphs uncovering possible previously unnoticed associations based on context information. To rank these subgraphs, we implement a novel ranking method using the context information obtained by performing random walks on the graph. In addition, we enhance the system by training a Neural Network Classifier to output the likelihood of the two concepts being likely related, which provides better insights to the end user.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc1538709
Date08 1900
CreatorsDharmavaram, Sirisha
ContributorsJin, Wei, Guo, Xuan, Yuan, Xiaohui
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatv, 60 pages, Text
RightsUse restricted to UNT Community, Dharmavaram, Sirisha, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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