We aim to prepare the next generation of scientists and engineers to address the monumental challenge of multi-type biomedical big data manipulation, analysis, and interpretation. We propose a curriculum and a set of programmatic activities to create an interdisciplinary training ground wherein teams of students will work across key disciplines, benefit from a true co-mentoring and interdisciplinary environment, and develop the technical and soft skills necessary to succeed as independent scientists making groundbreaking new discoveries enabled by biomedical big data. Three key features of this proposed training program are (1) depth in Big Data technical training, (2) tangible soft skill training through collaborative, team science activities, and (3) cross-disciplinary co-mentors in close physical proximity. Our proposed program embraces the philosophy that there can be no question about the productivity and effectiveness of research teams formed of partners with diverse expertise. (The National Academies, 2004). We propose courses, symposia, workshops, and collaborative activities to create a training environment that will support the development of the next generation of biomedical big data scientists and engineers. The proposed program will necessarily lie outside the existing traditional curricular structure, and it will provide the blueprint for the future in which collaborative biomedical big data science will play an ever-increasing role in biomedical science research. The program is led by faculty with a strong history of prior collaboration and activity in biomedical big data science. A total of 8 trainees will be supported at any given time, with 4 new trainees per year each with two years of support (with a total of 20 trainees over the lifetime of the grant). The goals of our proposed training program are to (1) Create innovative and effective approaches to teaching collaborative methods for interdisciplinary biomedical big data science; (2) Address the demand at UVa and nationally for students and ultimately scientific professionals with data science expertise who can work on interdisciplinary teams to address complex challenges and problems; (3) Produce a scalable, sustainable and transferable program for education and training in collaborative big data science; (4) Create new pipelines for Ph.D. students from underrepresented groups. Recognizing the inextricable link between diversity and excellence, our program seeks to ensure that the next generation of leaders in biomedical big data science and engineering emerges from a variety of backgrounds. With an excellent infrastructure and history of recruiting students from underrepresented groups to existing NIH and other federal agency-funded programs at UVa, this proposed training program will flourish in bringing diversity to biomedical big data science. Historically, diverse sets of expertise were deeply embedded in the solution to many important scientific problems. We seek to imbue this sense of dedication to collaboration in our training program on biomedical big data.
We propose a curriculum and a set of programmatic activities to create an interdisciplinary training ground wherein teams of students will work across key disciplines, benefit from a true co-mentoring and interdisciplinary environment, and develop the technical and 'soft' skills necessary to succeed as independent scientists making groundbreaking new discoveries enabled by biomedical big data.
|Robertson, Catherine C; Rich, Stephen S (2018) Genetics of type 1 diabetes. Curr Opin Genet Dev 50:7-16|
|Medlock, Gregory L; Carey, Maureen A; McDuffie, Dennis G et al. (2018) Inferring Metabolic Mechanisms of Interaction within a Defined Gut Microbiota. Cell Syst 7:245-257.e7|
|Moutinho Jr, Thomas J; Panagides, John C; Biggs, Matthew B et al. (2017) Novel co-culture plate enables growth dynamic-based assessment of contact-independent microbial interactions. PLoS One 12:e0182163|
|Lanchantin, Jack; Singh, Ritambhara; Wang, Beilun et al. (2017) DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS. Pac Symp Biocomput 22:254-265|
|Bartelt, Luther A; Bolick, David T; Mayneris-Perxachs, Jordi et al. (2017) Cross-modulation of pathogen-specific pathways enhances malnutrition during enteric co-infection with Giardia lamblia and enteroaggregative Escherichia coli. PLoS Pathog 13:e1006471|
|Singh, Ritambhara; Lanchantin, Jack; Sekhon, Arshdeep et al. (2017) Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin. Adv Neural Inf Process Syst 30:6785-6795|
|Bolick, David T; Mayneris-Perxachs, Jordi; Medlock, Greg L et al. (2017) Increased Urinary Trimethylamine N-Oxide Following Cryptosporidium Infection and Protein Malnutrition Independent of Microbiome Effects. J Infect Dis 216:64-71|
|Biggs, Matthew B; Medlock, Gregory L; Moutinho, Thomas J et al. (2017) Systems-level metabolism of the altered Schaedler flora, a complete gut microbiota. ISME J 11:426-438|
|Mayneris-Perxachs, Jordi; Bolick, David T; Leng, Joy et al. (2016) Protein- and zinc-deficient diets modulate the murine microbiome and metabolic phenotype. Am J Clin Nutr 104:1253-1262|