The sequencing of the human genome has set the stage for the next major frontier in biology, the proteome, and has paved the way for the new """"""""omics"""""""" era in biomedical reseach. The exploding cascadeof data requires integration of bioinformatics tools for data representation and manipulation and sophisticated statistical and mathematical approaches for validating, analyzing, modeling and, finally, unifying and interpreting the myriad of """"""""omic"""""""" data to infer biological function.
The aim of the Biostatistics Training for Basic Biomedical Research (BTBBR) program, proposed by the Department of Biostatistics, Bioinformatics, and Epidemiology at the Medical University of South Carolina (MUSC), is to train a new generation of biostatisticians who have substantial didactic and hands-on experience in the basic biomedical sciences so that they are prepared to assume key roles in this new generation of basic biomedical research. The BTBBR program stresses the integration of biostatistical theory and methods, including nonlinear systems analysis and mathematical modeling, with tools from bioinformatics to address quantitative frontiers in modern multi-disciplinary biological research. The program will capitalize on an established and successful college-wide program.offering a common basic science curriculum that providesstructured, broad-based didactic and laboratory training in the.basic biomedical sciences at an entry level;an established biostatistics program that emphasizes integration of biological knowledge and biostatistical principles;and an atmosphere of acknowledged need for biostatistical input among basic biomedical science researchers in four focus areas in which MUSC has nationally recognized strength: proteomics, lidipomics, signaling, and neurobiology.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
3T32GM074934-05S1
Application #
7886099
Study Section
Special Emphasis Panel (ZGM1-BRT-6 (BS))
Program Officer
Gaillard, Shawn R
Project Start
2009-09-01
Project End
2011-08-31
Budget Start
2009-09-01
Budget End
2011-08-31
Support Year
5
Fiscal Year
2009
Total Cost
$74,418
Indirect Cost
Name
Medical University of South Carolina
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
183710748
City
Charleston
State
SC
Country
United States
Zip Code
29425
Prince Nelson, Sybil L; Ramakrishnan, Viswanathan; Nietert, Paul J et al. (2017) An evaluation of common methods for dichotomization of continuous variables to discriminate disease status. Commun Stat Theory Methods 46:10823-10834
Shotwell, Mary E; McFee, Wayne E; Slate, Elizabeth H (2016) A Bayesian mixture model for missing data in marine mammal growth analysis. Environ Ecol Stat 23:585-603
Wolf, Bethany J; Slate, Elizabeth H; Hill, Elizabeth G (2015) Ordinal Logic Regression: A classifier for discovering combinations of binary markers for ordinal outcomes. Comput Stat Data Anal 82:152-163
Spainhour, John Christian G; Janech, Michael G; Schwacke, John H et al. (2014) The application of Gaussian mixture models for signal quantification in MALDI-TOF mass spectrometry of peptides. PLoS One 9:e111016
Qin, Tingting; Matmati, Nabil; Tsoi, Lam C et al. (2014) Finding pathway-modulating genes from a novel Ontology Fingerprint-derived gene network. Nucleic Acids Res 42:e138
Schwacke, John H; Spainhour, John Christian G; Ierardi, Jessalyn L et al. (2013) Network modeling reveals steps in angiotensin peptide processing. Hypertension 61:690-700
Rachidi, Saleh M; Qin, Tingting; Sun, Shaoli et al. (2013) Molecular profiling of multiple human cancers defines an inflammatory cancer-associated molecular pattern and uncovers KPNA2 as a uniform poor prognostic cancer marker. PLoS One 8:e57911
Ramakrishnan, Viswanathan; Thacker, Leroy R (2012) POPULATION ATTRIBUTABLE FRACTION AS A MEASURE OF HERITABILITY IN DICHOTOMOUS TWIN DATA. Commun Stat Simul Comput 41:
Qin, Tingting; Tsoi, Lam C; Sims, Kellie J et al. (2012) Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network. BMC Syst Biol 6 Suppl 3:S3
Tsoi, Lam C; Qin, Tingting; Slate, Elizabeth H et al. (2011) Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior. BMC Bioinformatics 12:438

Showing the most recent 10 out of 14 publications