Jeffrey Thompson, Ph.D.
Assistant Professor, Department of Biostatistics & Data Science
B.S., Computer Science, University of Southern Maine, Portland, ME
Ph.D., Quantitative Biomedical Science, Dartmouth College, Hanover, NH
2016 - Best Poster, Integrative RNA Biology Special Interest Group at ISMB
2017 - Travel grant to present at ISMB 2016, Neukom Institute
2017 - Travel grant to present at PSB 2017, Pacific Symposium on Biocomputing
Statistical/machine learning methods, data integration, feature selection, quantitative 'omics, molecular epidemiology, survival analysis, and predictive models.
Personal Mission Statement
My interests revolve around ways to take better advantage of the massive amounts of biomedical data that are now available. In particular, I am interested in the development and application of multi-omic or trans-omic data integration methods to create biomarkers that are more accurate and generate additional biological insight by creating more holistic models. Typically, when analyzing such big datasets, it is necessary to select relevant features of the data for downstream analysis. Thus, methods for selecting the most promising features are of great interest to me. I think it is always important to consider more than just 'omic data, and clinical variables frequently capture different and complimentary information. To that end, I am also working on approaches for extracting clinical information from free text fields in electronic health records, to facilitate clinical trials, and to make it easier to annotate datasets with the maximum available information. Furthermore, data integration can involve more than just combining different types of data for individual subjects. My interests also include leveraging data between studies (meta-analysis), and determining the genomic basis for comorbidities by integrating data from studies of different diseases.
1) Thompson JA, Marsit CJ. A Methylation-to-Expression Feature Model for Generating Accurate Prognostic Risk Scores And Identifying Disease Targets In Clear Cell Kidney Cancer. Pac Symp Biocomput. 2016;22:509-520. PubMed PMID: 27897002; PubMed Central PMCID: PMC5177986.
2) Thompson JA, Tan J, Greene CS. Cross-platform normalization of microarray and RNA-seq data for machine learning applications. PeerJ. 2016 Jan 21;4:e1621. doi: 10.7717/peerj.1621. eCollection 2016. PubMed PMID: 26844019; PubMed Central PMCID: PMC4736986.
3) Peterson SM, Thompson JA, Ufkin ML, Sathyanarayana P, Liaw L, Congdon CB. Common features of microRNA target prediction tools. Front Genet. 2014 Feb 18;5:23. doi: 10.3389/fgene.2014.00023. eCollection 2014. Review. PubMed PMID: 24600468; PubMed Central PMCID: PMC3927079.
4) Thompson JA, Congdon CB. GAMI-CRM: Using de novo motif inference to detect cis-regulatory modules. IEEE Congress on Evolutionary Computation (2014). 2014 July. 1022-1029. DOI: 10.1145/2506583.2506689
5) Thompson JA, Congdon CB. An exploration into improving DNA motif inference by looking for highly conserved core regions. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 2013 April. 60-67. DOI: 10.1109/CIBCB.2013.6595389.
6) Wang Q, Arighi CN, King BL, Polson SW, Vincent J, Chen C, Huang H, Kingham BF, Page ST, Rendino MF, Thomas WK, Udwary DW, Wu CH; North East Bioinformatics Collaborative Curation Team.. Community annotation and bioinformatics workforce development in concert--Little Skate Genome Annotation Workshops and Jamborees. Database (Oxford). 2012 Mar 20;2012:bar064. doi: 10.1093/database/bar064. Print 2012. PubMed PMID: 22434832; PubMed Central PMCID: PMC3308154.
Thompson JA, Christensen BC. Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts.. BioRxiv: 187526 [Preprint]. 2017 September 12. DOI: https://doi.org/10.1101/187526.