printer friendly pageBIOMEDIN 217: Translational Bioinformatics (BIOE 217, CS 275)
Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies and course project. Prerequisites: programming ability at the level of
CS 106A and familiarity with biology and statistics.
Terms: Win
| Units: 4
Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
...
more instructors for BIOMEDIN 217 »
Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
;
Gloudemans, M. (TA)
;
McInnes, G. (TA)
;
Shenoy, A. (TA)
BIOMEDIN 218: Translational Bioinformatics Lectures
Same content as
BIOMEDIN 217; for medical and graduate students who attend lectures and participate in limited assignments and final project. Computational methods for the translation of biomedical data into diagnostic, prognostic, and therapeutic applications in medicine. Topics: multi-scale omics data generation and analysis, utility and limitations of public biomedical resources, machine learning and data mining, issues and opportunities in drug discovery, and mobile/digital health solutions. Case studies.nPrerequisites: programming ability at the level of
CS 106A and familiarity with biology and statistics.
Terms: Win
| Units: 2
Instructors:
Gevaert, O. (PI)
;
Wall, D. (PI)
GENE 217: Translational Bioinformatics
(Same as
BIOMEDIN 217,
CS 275) Analytic, storage, and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; analysis of microarray data; analysis of polymorphisms, proteomics, and protein interactions; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies. Prerequisites: programming ability at the level of
CS 106A and familiarity with statistics and biology.
Terms: Win
| Units: 4
Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
...
more instructors for GENE 217 »
Instructors:
Gevaert, O. (PI)
;
Mallick, P. (PI)
;
Wall, D. (PI)
;
Gloudemans, M. (TA)
;
McInnes, G. (TA)
;
Shenoy, A. (TA)
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