The Computation and Informatics in Biology and Medicine (CIBM) training program is helping to produce the next generation of researchers of biomedical problems with strengths in both informatics and biology. The interplay between computational and statistical methods in the biomedical sciences continues to expand rapidly so that now both computer modeling and informatics play key roles. Our faculty and trainees have been developing robust algorithms for the analysis of molecular data. Increasingly, questions in the biosciences are being phrased for these more quantitative approaches, and computer scientists are discovering new computational approaches in attempting to address biological problems. Furthermore, the power of new hardware, algorithms, and software is transforming our thinking about complex systems research. These advances are only possible when computer scientists understand enough about the problems to design usable tools and when bioscientists understand what is possible using computational and information technologies. CIBM has focused on the development of novel bioinformatics algorithms to analyze molecular data, including genome sequences, proteins (levels, interactions, structures), and regulatory pathways. We propose that for phase two of the program that we continue this focus and to further distinguish our program by adding a unique translational medicine component. In a collaboration with the Marshfield Clinic, our trainees will have the added opportunity to develop algorithms to predict clinical parameters, such as disease susceptibility or treatment response, from combined molecular and clinical data. A strong training program, including a multidisciplinary core curriculum has been developed at CIBM, and the environment at the University of Wisconsin in biology and computational sciences provides a rich setting for research training at the graduate and postdoctoral level.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
3T15LM007359-09S1
Application #
8129106
Study Section
Special Emphasis Panel (ZLM1-AP-T (O1))
Program Officer
Florance, Valerie
Project Start
2002-07-01
Project End
2012-06-30
Budget Start
2010-07-01
Budget End
2011-06-30
Support Year
9
Fiscal Year
2010
Total Cost
$94,834
Indirect Cost
Name
University of Wisconsin Madison
Department
Biochemistry
Type
Schools of Earth Sciences/Natur
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Darst, Burcu F; Malecki, Kristen C; Engelman, Corinne D (2018) Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet 19:65
Cesnik, Anthony J; Yang, Bing; Truong, Andrew et al. (2018) Long Noncoding RNAs AC009014.3 and Newly Discovered XPLAID Differentiate Aggressive and Indolent Prostate Cancers. Transl Oncol 11:808-814
Overmyer, Katherine A; Tyanova, Stefka; Hebert, Alex S et al. (2018) Multiplexed proteome analysis with neutron-encoded stable isotope labeling in cells and mice. Nat Protoc 13:293-306
Huang, Xiayuan; Elston, Robert C; Rosa, Guilherme J et al. (2018) Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics 34:635-642
Schaffer, Leah V; Rensvold, Jarred W; Shortreed, Michael R et al. (2018) Identification and Quantification of Murine Mitochondrial Proteoforms Using an Integrated Top-Down and Intact-Mass Strategy. J Proteome Res 17:3526-3536
Schaffer, Leah V; Shortreed, Michael R; Cesnik, Anthony J et al. (2018) Expanding Proteoform Identifications in Top-Down Proteomic Analyses by Constructing Proteoform Families. Anal Chem 90:1325-1333
Cesnik, Anthony J; Shortreed, Michael R; Schaffer, Leah V et al. (2018) Proteoform Suite: Software for Constructing, Quantifying, and Visualizing Proteoform Families. J Proteome Res 17:568-578
Romano, Kymberleigh A; Dill-McFarland, Kimberly A; Kasahara, Kazuyuki et al. (2018) Fecal Aliquot Straw Technique (FAST) allows for easy and reproducible subsampling: assessing interpersonal variation in trimethylamine-N-oxide (TMAO) accumulation. Microbiome 6:91
Hong, Jaeyoung; Hatchell, Kathryn E; Bradfield, Jonathan P et al. (2018) Transethnic Evaluation Identifies Low-Frequency Loci Associated With 25-Hydroxyvitamin D Concentrations. J Clin Endocrinol Metab 103:1380-1392
Darst, Burcu; Engelman, Corinne D; Tian, Ye et al. (2018) Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20. BMC Genet 19:76

Showing the most recent 10 out of 239 publications