Jeffrey A. Thompson, Ph.D.

Professional Background

Jeffrey A. Thompson received his PhD in Quantitative Biomedical Science from Dartmouth College, focusing on the application of machine learning and data integration techniques to molecular epidemiology. He is PI of the Curated Cancer Clinical Outcomes Database (C3OD) for the KU Cancer Center. C3OD integrates information from electronic medical records, a tumor registry, a biospecimen repository, and a clinical trial management system to facilitate cancer research by providing easy-to-use software for feasibility projection for cancer studies, automated clinical trial pre-screening, and detailed search capabilities for biospecimens, among other capabilities. Additionally, Dr. Thompson is leveraging his expertise in deep learning to develop natural language processing capabilities for handling the unstructured data that is inherent to electronic medical records. Furthermore, Dr. Thompson co-directs a team of informaticists and data scientists that are working to create, deploy, and integrate a world-class set of tools for all aspects of research, including data collection, clinical trial management, accrual monitoring, screening, research drug dispensation, and more. As Associate Director of the Quantitative Omics Core for the Kansas Institute of Precision Medicine COBRE, he helps support a range of precision medicine projects and is working to develop new methods for interrogating genomic data to gain a more holistic understanding of disease.

• BS, Computer Science, University of Southern Maine, Portland, ME
• PhD, Quantitative Biomedical Science, Dartmouth College, Hanover, NH

• American Statistical Association, Member, 2017 - Present

Research

Overview

Dr. Thompson's research covers two areas. The first area is in facilitate clinical research through improving research informatics infrastructure. In part, this involves developing intuitive informatics tools for researchers to use, such as C3OD, but it also involves work to extract meaningful information from unstructured data using machine learning (e.g. automatic extraction of data from notes in the EHR). The second area is in statistical genomics, for which Dr. Thompson is researching ways to create holistic cancer models that integrate a range of molecular data to improve prognostic modeling, and help us to better understand cancer etiology.

• Mudaranthakam, D.., P., Thompson, J., Hu, J., Pei, D., Chintala, S.., R., Park, M., Fridley, B.., L., Gajewski, B., Koestler, D.., C., Mayo, M.., S.. 2018. A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials. JAMIA Open, 1 (2), 166-171. https://academic.oup.com/jamiaopen/article/1/2/166/5051720
• Thompson, Jeffrey., A., Koestler, Devin., C.. 2020. Equivalent Change Enrichment Analysis: Assessing Equivalent and Inverse Change in Biological Pathways between Diverse Experiments. BMC Genomics, 21 (1), 180. https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-020-6589-x
• Thompson, J.., A., Christensen, B.., C., Marsit, C.., J.. 2018. Methylation-to-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, and Pathologic Complete Response in Multiple Cohorts. Scientific Reports, 8. https://www.nature.com/articles/s41598-018-23494-0
• Thompson, J.., A., Christensen, B.., C., Marsit, C.., J.. 2018. Pan-Cancer Analysis Reveals Differential Susceptibility of Bidirectional Gene Promoters to DNA Methylation, Somatic Mutations, and Copy Number Alterations. Int J Mol Sci, 19 (8). https://www.mdpi.com/1422-0067/19/8/2296
• Thompson, Jeffrey., A, Hu, Jinxiang, Mudaranthakam, Dinesh., Pal, Streeter, David, Neums, Lisa, Park, Michele, Koestler, Devin., C, Gajewski, Byron, Jensen, Roy, Mayo, Matthew., S. 2019. Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.. Scientific Reports, 9 (1), 9253. https://www.nature.com/articles/s41598-019-45705-y
• Mudaranthakam, Dinesh., Pal, Shergina, Elena, Thompson, Jeffrey, Streeter, David, Hu, Jinxiang, Gajewski, Byron, Koestler, Devin., C., Godwin, Andrew, Jensen, Roy, Mayo, Matthew., S.. 2019. Optimizing retrieval of bio specimens using a curated cancer clinical outcomes database (C3OD). Cancer Informatics. https://journals.sagepub.com/doi/full/10.1177/1176935119886831
• Mudaranthakam, Dinesh., Pal, Krebill, Ron, Singh, Ravi., D., Price, Cathy, Thompson, Jeffrey, Gajewski, Byron, Koestler, Devin, Mayo, Matthew., S.. 2019. Case Study: Electronic Data Capture System Validation at an Academic Institution. Data Basics, 25 (2)
• Elsarraj, Hanan, Hong, Yan, Limback, Darlene, Zhao, Ruonan, Berger, Jenna, Bishop, Stephanie, Sabbagh, Aria, Oppenheimer, Linzi, Harper, Haleigh., E, Tsimelzon, Anna, Huang, Shixia, Hilsenbeck, Susan, Edwards, Dean, Fontes, Joseph, Fan, Fang, Madan, Rashna, Fangman, Ben, Ellis, Ashley, Tawfik, Ossama, Persons, Diane, Fields, Timothy, Godwin, Andrew, Hagan, Christy, Swenson-Fields, Katherin, Coarfa, Cristian, Thompson, Jeffrey, Behbod, Fariba. 2020. BCL9/STAT3 Regulation of Transcriptional Enhancer Networks Promote DCIS Progression. npg Breast Cancer, 6 (12). https://www.nature.com/articles/s41523-020-0157-z
• Meier, Richard, Thompson, Jeffrey., A., Chung, Mei, Zhao, Naisi, Kelsey, Karl., T., Michaud, Dominique., S., Koestler, Devin., C.. 2019. Bayesian framework for identifying consistent patterns of microbial abundance between body sites. . Statistical Applications in Genetics and Molecular Biology, 18 (6). https://www.degruyter.com/document/doi/10.1515/sagmb-2019-0027/html
• Steigerwalt, Kristy, Fitterling, Lori, Harvey, Mariah, Kartonis, Sarah., McQueeny, DeGeus, Marilyn, Franco, Nora, Thompson, Marie, Sykes-Berry, Sue, Mullaly-Quijas, Peggy, Thompson, Jeffrey., A. 2018. Health Sciences Patron Preference for Library Spaces: A Multi-Site Observational Study. Medical References Services Quarterly. https://www.tandfonline.com/doi/full/10.1080/02763869.2018.1548890?scroll=top&needAccess=true