Literature Mining and Ontology Mapping Applied to Big Data
Abstract
Discovering the network of associations and relationships among diseases, genes, and risk factors is critical in clinical and translational research. The goal of this study was to design a system that would enable strategic reading/filtering and reduce information overload, generate new hypotheses, bridge the knowledge gap, and develop “smart apps.” We present the implementation of a text analytic system, Adaptive Robust and Integrative Analysis for Finding Novel Associations (ARIANA). The system is context-specific, modular, and scalable and able to capture direct and indirect associations among 2,545 biomedical concepts. An easy-to-use Web interface was developed to query, interact, and visualize the results. Empirical studies showed that the system was able to find novel associations and generate new hypotheses. For instance, the system captured the association between the drug hexamethonium and pulmonary fibrosis, which in 2001 caused the tragic death of a healthy volunteer. The software is available with a properly executed end-user licensing agreement at http://www.ARIANAmed.org.
Publication Title
Application of Big Data for National Security: A Practitioner's Guide to Emerging Technologies
Recommended Citation
Abedi, V., Yeasin, M., & Zand, R. (2015). Literature Mining and Ontology Mapping Applied to Big Data. Application of Big Data for National Security: A Practitioner's Guide to Emerging Technologies, 184-208. https://doi.org/10.1016/B978-0-12-801967-2.00013-6
