Text Mining Adoption for Pharmacogenomics-based Drug Discovery in a Large Pharmaceutical Company: a Case Study
Text mining can help pharmacogenomics researchers reduce information overload hindering pharmacogenomics-based drug discovery (PGx-DD) because it can aid in the generation of rich novel information from large collections of diverse scientific literature and research data. The present study aims to understand text mining adoption and innovation for PGx-DD in the pharmaceutical industry. The study re-frames text mining as an approach to automate the generation of novel information, reviews successful exemplary text mining applications, and examines a case study of a leading pharmaceutical company within the novelty generation framework. The case study demonstrates that the Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, Davis, & Davis, 2003) does not account for conceptual barriers to adoption and innovation. By Everett Rogers’ Diffusion of innovation theory (1983), the case study subject is more of an early adopter rather than an innovator. In order to fulfill the promise of PGx-DD, drug companies may need to re-conceptualize text mining by focusing on its capacity to generate novel high-quality information and subsequently return to a higher-risk path of innovation.
Advisor:Stephanie W. Haas
School:University of North Carolina at Chapel Hill
School Location:USA - North Carolina
Source Type:Master's Thesis
Keywords:diffusion of innovations—case studies drug discovery pharmaceutical industry—technological pharmacogenomics text mining
Date of Publication:11/15/2006