Precision medicine seeks to develop evidence-based approaches to “personalizing” treatment to the characteristics of individual patients, thereby providing health care professionals with informed and principled decision support. This emerging field has become a central focus of the nation’s health sciences research agenda.
Essential to precision medicine are quantitative methods for translating heterogeneous data sources into actionable information to guide treatment decisions. Mathematical, statistical, and computational scientists have favored different approaches to this challenge:
(i) Mathematicians have focused on developing detailed, multi-scale theoretical models of biological, physiological, and other mechanisms underlying disease and treatment response, refining and tuning these models based on data, and using them to guide treatment decisions;
(ii) Statisticians have developed conceptual frameworks and associated methodology that acknowledge the possibly complicated, time-dependent confounding of patient characteristics and treatment decisions in observational data, which can distort relationships and lead to erroneous results if not taken into proper account; and
(iii) Computational scientists have devised strategies for harnessing the complex, massive, and disparate data resources that must be synthesized.
All of these perspectives are critical to surmounting the challenges of precision medicine. Their integration has the potential to lead to fundamentally new methodological advances, accelerating progress toward widespread deployment of precision medicine in practice. This will require mathematical, statistical, and computational scientists to forge new, deep collaborations among themselves and with biomedical scientists.
This SAMSI program facilitated this critical interdisciplinary exchange by bringing together leading mathematical, statistical, computational, and health sciences researchers to pursue innovative, data-driven methodology for precision medicine. The program fostered these interactions through workshops, opportunities for long- and short-term visitors, and theme-focused working groups that meet regularly by video-conference, allowing ongoing collaborations among non-resident participants.
WG1: Sequential Decision Making, Dan Lizotte, University of Western Ontario / Erica Moodie, McGill University
WG2: Observational Microbiome, Li Ma, Duke University
WG3: mHealth, Rumi Chunara, New York University
WG4: Neural Networks in Biomedicine, Michael Mayhew, Inflammatix
WG5: Model Learning of Model Selection, TBD/Heiko Enderling, Moffitt Cancer Center
** NOTE: WG6 has become a subgroup of WG1 **
WG7: Tumor Heterogeneity, Kevin Flores, NCSU / Erica Rutter, NCSU / John Nardini, SAMSI/NCSU
WG8: Clinical Trials in Precision Medicine, Elizabeth Slate, Florida State University
Planning for this program is ongoing. As more information becomes available, it will be provided.
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