Epidemiology & Biostatistics
The Wesley Tansey Lab
The Tansey lab focuses on solving frontier problems in cancer data science through the development of innovative statistical machine learning methods. How do we discover effective combination therapies when the search space of possible combinations is vast? Are there patterns of spatial architecture in the tumor microenvironment that predict whether a patient will respond to a specific therapy? How do we build powerful-yet-interpretable multimodal models of medical images, laboratory tests, and clinical records that can inform and improve treatment decisions in the clinic? The goal of our lab is to distill these kinds of important scientific questions into precise mathematical statements, then derive answers in the form of computationally efficient and statistically principled methods. We are interested in a number of areas in statistics and computer science, including graphical models, Bayesian methods, deep learning, hypothesis testing, conditional density estimation, spatial statistics, active learning, and causal inference. Ultimately, we seek to lay the statistical and computational foundations necessary to deliver on the promise of precision medicine: delivering the right treatment, for the right patient, at the right moment, and at the right dose.
Zhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst. 2023 Jul 19;14(7):605-619.e7. doi: 10.1016/j.cels.2023.06.003. PMID: 37473731; PMCID: PMC10368078.
Freeman BA, Jaro S, Park T, Keene S, Tansey W, Reznik E. MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization. Genome Biol. 2022 Sep 1;23(1):184. doi: 10.1186/s13059-022-02738-3. PMID: 36050754; PMCID: PMC9438248.
Tansey W, Li K, Zhang H, Linderman SW, Rabadan R, Blei DM, Wiggins CH. Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach. Biostatistics. 2022 Apr 13;23(2):643-665. doi: 10.1093/biostatistics/kxaa047. PMID: 33417699; PMCID: PMC9007438.
Sudarshan M, Tansey W, Ranganath R. Deep direct likelihood knockoffs. Adv Neural Inf Process Syst. 2020 Dec;33:5036-5046. PMID: 33953523; PMCID: PMC8096517.
Wesley Tansey, PhD
- The Tansey lab focuses on solving frontier problems in cancer data science through the development of innovative statistical machine learning methods.
- PhD in Computer Science, University of Texas at Austin
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Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.
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Wesley Tansey discloses the following relationships and financial interests:
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