DMCA
Context-Driven Automatic Subgraph Creation for Literature-Based Discovery
BibTeX
@MISC{Camerona_context-drivenautomatic,
author = {Delroy Camerona and Ramakanth Kavulurub and Thomas C. Rindfleschc and Amit P. Shetha and Krishnaprasad Thirunarayana},
title = {Context-Driven Automatic Subgraph Creation for Literature-Based Discovery},
year = {}
}
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Abstract
Background: Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: 1) domain expertise and structured background knowledge to manually filter and explore the literature, 2) distributional statistics and graph-theoretic measures to rank interesting connections and 3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of a priori knowledge, heuristics and manual filtering is still required. Objectives: In this paper we implement and evaluate a context-driven, automatic subgraph creation method that captures multifaceted complex associations between biomedical concepts for LBD. Given a pair of concepts, our method automatically generates a ranked list of subgraphs, which provide informative and potentially unknown associations between such concepts. Methods: To generate subgraphs, the set of all MEDLINE articles that contain either of two specified concepts (A, C) are first collected. Binary relationships or assertions, which are automatically extracted from the MEDLINE articles, called semantic predications, are then used to create a labeled directed predications graph. In this graph, a path is represented as a sequence of semantic predications. The hierarchical agglomerative clustering (HAC) algorithm is then applied to cluster paths, which are
Keyphrases
literature-based discovery context-driven automatic subgraph creation medline article semantic predication understood association priori knowledge complex association non-interacting scientific literature biomedical concept hierarchical agglomerative clustering graph-theoretic measure considerable degree binary relationship lbd include use interesting connection background knowledge manual approach spurious connection distributional statistic domain expertise specified concept several graph-based approach unknown association labeled directed predication graph hidden association purely distributional approach prior approach ranked list manual filtering automatic subgraph creation method
