Support is requested for a multidisciplinary predoctoral training program in Quantitative Biomedical Sciences (QBS) at Dartmouth College. The QBS program was founded in 2010 to provide cross-training in quantitative, computational and biomedical sciences to prepare students for a new research landscape that is increasingly characterized by complex problems, big data and multidisciplinary teams. The QBS program spans the entire Dartmouth College campus including faculty from the Schools of Arts and Sciences, Business, Engineering and Medicine. QBS admits 6-8 students per year from different backgrounds including computer science, mathematics, biology, chemistry, and engineering. These students are selected from a highly qualified national and international pool of candidates that are rich in both ethnic and racial diversity. As part of the proposed T32 training program, we will select two highly qualified predoctoral students each year for two years of support based on academic qualifications and a dissertation project that will focus on big data. The predoctoral trainees will complete an eight course core curriculum that will include two courses in computer science or data science, two courses in bioinformatics, two courses in biostatistics, two courses in epidemiology and a two course sequence in integrative biomedical sciences that exposes students to interdisciplinary research and that culminates in a collaborative big data class project. Students in this program take two elective courses, one term of teaching, journal clubs and seminars, training in responsible conduct of research, a written and oral qualifier exam, a yearly research in progress seminar, and the completion of a significant research project that forms the foundation of the written dissertation that is orally defended. Faculty trainers include QBS and computer science faculty that have extramural funding, a strong track record of graduate training experience, a strong track record of big data research and a commitment to participating in the activities necessary for successful predoctoral training. Research areas of the faculty include bioinformatics, biostatistics, computer science, computational biology, genomics, and epidemiology. It is our vision that the next generation of big data researchers need multiple skills and expertise from areas such as bioinformatics, biostatistics, computer science and epidemiology to bring together teams of researchers.

Public Health Relevance

Support is requested for a multidisciplinary predoctoral training program in Quantitative Biomedical Sciences (QBS) at Dartmouth College. It is our vision that the next generation of big data researchers need multiple skills and expertise from areas such as bioinformatics, biostatistics, and epidemiology to bring together teams of researchers from diverse fields to tackle the most challenging problems in biomedicine.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Institutional National Research Service Award (T32)
Project #
5T32LM012204-02
Application #
9248415
Study Section
Special Emphasis Panel (ZRG1-IMST-T (50)R)
Program Officer
Ye, Jane
Project Start
2016-04-01
Project End
2021-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
2
Fiscal Year
2017
Total Cost
$191,228
Indirect Cost
$9,424
Name
Dartmouth College
Department
Type
Schools of Medicine
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
03755
Rees, Christiaan A; Nasir, Mavra; Smolinska, Agnieszka et al. (2018) Detection of high-risk carbapenem-resistant Klebsiella pneumoniae and Enterobacter cloacae isolates using volatile molecular profiles. Sci Rep 8:13297
Nasir, Mavra; Bean, Heather D; Smolinska, Agnieszka et al. (2018) Volatile molecules from bronchoalveolar lavage fluid can 'rule-in' Pseudomonas aeruginosa and 'rule-out' Staphylococcus aureus infections in cystic fibrosis patients. Sci Rep 8:826
Purcaro, Giorgia; Rees, Christiaan A; Melvin, Jeffrey A et al. (2018) Volatile fingerprinting of Pseudomonas aeruginosa and respiratory syncytial virus infection in an in vitro cystic fibrosis co-infection model. J Breath Res 12:046001
Crow, Rebecca S; Lohman, Matthew C; Titus, Alexander J et al. (2018) Mortality Risk Along the Frailty Spectrum: Data from the National Health and Nutrition Examination Survey 1999 to 2004. J Am Geriatr Soc 66:496-502
Rees, Christiaan A; Burklund, Alison; Stefanuto, Pierre-Hugues et al. (2018) Comprehensive volatile metabolic fingerprinting of bacterial and fungal pathogen groups. J Breath Res 12:026001
Purcaro, Giorgia; Rees, Christiaan A; Wieland-Alter, Wendy F et al. (2018) Volatile fingerprinting of human respiratory viruses from cell culture. J Breath Res 12:026015
Brooks, J M; Titus, A J; Bruce, M L et al. (2018) Depression and Handgrip Strength Among U.S. Adults Aged 60 Years and Older from NHANES 2011-2014. J Nutr Health Aging 22:938-943
Wilkins, Owen M; Titus, Alexander J; Gui, Jiang et al. (2017) Genome-scale identification of microRNA-related SNPs associated with risk of head and neck squamous cell carcinoma. Carcinogenesis 38:986-993
Qian, David C; Molfese, David L; Jin, Jennifer L et al. (2017) Genome-wide imaging association study implicates functional activity and glial homeostasis of the caudate in smoking addiction. BMC Genomics 18:740
Titus, Alexander J; Way, Gregory P; Johnson, Kevin C et al. (2017) Deconvolution of DNA methylation identifies differentially methylated gene regions on 1p36 across breast cancer subtypes. Sci Rep 7:11594

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