Biomedicine as a data driven science - Bourne, Philip E., author; Colorado State University, publisher (Colorado State University. Libraries, 2015)
Presented at the National data integrity conference: enabling research: new challenges & opportunities held on May 7-8, 2015 at Colorado State University, Fort Collins, Colorado. Researchers, administrators and integrity officers are encountering new challenges regarding research data and integrity. This conference aims to provide attendees with both a high level understanding of these challenges and impart practical tools and skills to deal with them. Topics will include data reproducibility, validity, privacy, security, visualization, reuse, access, preservation, rights and management.
Philip Bourne is the Associate Director for Data Science, National Institute of Health. Dr. Bourne comes to the NIH from the University of California San Diego, where he is the Associate Vice Chancellor for Innovation and Industry Alliances of the Office of Research Affairs and a Professor in the Department of Pharmacology and the Skaggs School of Pharmacy and Pharmaceutical Sciences. He also is the Associate Director of the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank. Dr. Bourne was trained as a physical chemist and obtained his Ph.D. from The Flinders University in South Australia. His professional interests focus on relevant biological and educational outcomes derived from computation and scholarly communication. This work involves the use of algorithms, text mining, machine learning, metalanguages, biological databases, and visualization applied to problems in systems pharmacology, evolution, cell signaling, apoptosis, immunology, and scientific dissemination. He has published over 300 papers and five books. One area to which he is extremely committed is to furthering the free dissemination of science through new models of publishing and better integration and subsequent dissemination of data and results.
PowerPoint presentation given on May 7, 2015.
Keywords: National Institute of Health; precision medicine; Research -- Data processing; Big data