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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2011: Genomics

Open Access Proceedings

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

Jayasimha Reddy Katukuri1*, Ying Xie2, Vijay V Raghavan1 and Ashish Gupta1

Author Affiliations

1 Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, Louisiana, 70504, USA

2 Department of Computer Science, Kennesaw State University, Kennesaw, Georgia, 30144, USA

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BMC Genomics 2012, 13(Suppl 3):S5  doi:10.1186/1471-2164-13-S3-S5

Published: 11 June 2012


Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper.