Stanford University has a strong record of training in both the biomedical sciences as well as computer science and engineering. It has hosted a degree program in Biomedical Informatics (BMI, formerly called Medical Information Sciences) since 1982, which has been a valuable proving ground for training at the intersection of biomedicine and computer science. With the increased demand for scientists with credentials in biocomputation, Stanford recognizes the need to greatly increase its capacity for training at this intersection. A faculty retreat in January 2000 involving more than 70 faculty (from the schools of Medicine, Engineering, Humanities & Sciences, and the Stanford Linear Accelerator) began a planning process that has lead to a two-part plan for organizing graduate training in biocomputation at Stanford. First, the size and the scope of the BMI degree program will be increased by expanding the research agenda to include six strategic areas identified by the faculty: (I) structural and functional genomics, (2) biomechanical simulation, (3) computer assisted interventions and robotics, (4) image acquisition and processing, (5) computer-assisted instruction and networked education, and (6) informatics, data modeling, and statistics. Second, the expanded BMI degree program will be used as a focal point for integrating students in disciplinary training programs (such as biology, genetics, computer science, or mechanical engineering) into the biomedical computation training environment. These mechanisms will include the creation of cross-disciplinary courses that teach fundamentals of biomedicine to technical graduate students, and teach fundamentals of these technical fields to biomedical graduate students. It will also include activities designed to encourage transfer of knowledge between BMI and disciplinary graduate students. The training grant will therefore fund both BIM students and disciplinary degree graduate students for an average of 3 years, and provide mechanisms for creating a cadre of young, well-qualified scientists equipped to tackle the scientific challenges that arise in the 21st century.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
1T32GM063495-01
Application #
6349633
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Zatz, Marion M
Project Start
2001-07-01
Project End
2004-06-30
Budget Start
2001-07-01
Budget End
2002-06-30
Support Year
1
Fiscal Year
2001
Total Cost
$73,180
Indirect Cost
Name
Stanford University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
800771545
City
Stanford
State
CA
Country
United States
Zip Code
94305
Faruque, Jessica; Rubin, Daniel L; Beaulieu, Christopher F et al. (2013) Modeling perceptual similarity measures in CT images of focal liver lesions. J Digit Imaging 26:714-20
Wong, J; Göktepe, S; Kuhl, E (2013) Computational modeling of chemo-electro-mechanical coupling: a novel implicit monolithic finite element approach. Int J Numer Method Biomed Eng 29:1104-33
John, Chand T; Anderson, Frank C; Higginson, Jill S et al. (2013) Stabilisation of walking by intrinsic muscle properties revealed in a three-dimensional muscle-driven simulation. Comput Methods Biomech Biomed Engin 16:451-62
John, Chand T; Seth, Ajay; Schwartz, Michael H et al. (2012) Contributions of muscles to mediolateral ground reaction force over a range of walking speeds. J Biomech 45:2438-43
Markova-Raina, Penka; Petrov, Dmitri (2011) High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes. Genome Res 21:863-74
Tepole, Adrián Buganza; Ploch, Christopher Joseph; Wong, Jonathan et al. (2011) Growing skin: A computational model for skin expansion in reconstructive surgery. J Mech Phys Solids 59:2177-2190
Abernethy, Neil F; DeRimer, Kathy; Small, Peter M (2011) Methods to identify standard data elements in clinical and public health forms. AMIA Annu Symp Proc 2011:19-27
Fox, Melanie D; Delp, Scott L (2010) Contributions of muscles and passive dynamics to swing initiation over a range of walking speeds. J Biomech 43:1450-5
Chang, Catie; Cunningham, John P; Glover, Gary H (2009) Influence of heart rate on the BOLD signal: the cardiac response function. Neuroimage 44:857-69
Chang, Catie; Thomason, Moriah E; Glover, Gary H (2008) Mapping and correction of vascular hemodynamic latency in the BOLD signal. Neuroimage 43:90-102

Showing the most recent 10 out of 21 publications