Advances in Text Mining and Visualization for Precision Medicine

Graciela Gonzalez-Hernandez¹, Abeed Sarker¹, Karen O'Connor¹, Casey Greene¹, Hongfang Liu²

¹Perelman School of Medicine, University of Pennsylvania
²Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine
Email: gragon@pennmedicine.upenn.edu, liu.hongfang@mayo.edu

Pacific Symposium on Biocomputing 23:559-565(2018)

© 2018 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Although the text mining community has explored this realm for some years, the official endorsement and funding launched in 2015 with the Precision Medicine Initiative are beginning to bear fruit. This session sought to elicit participation of researchers with strong background in text mining and/or visualization who are actively collaborating with bench scientists and clinicians for the deployment of integrative approaches in precision medicine that could impact scientific discovery and advance the vision of precision medicine as a universal, accessible approach at the point of care.