BIOMEDIN 216: Representations and Algorithms for Molecular Biology: Lectures
Lecture component of
BIOMEDIN 214. One unit for medical and graduate students who attend lectures only; may be taken for 2 units with participation in limited assignments and final project. Lectures also available via internet. Prerequisite: familiarity with biology recommended.
Terms: Aut
| Units: 1-2
Instructors:
Altman, R. (PI)
BIOMEDIN 217: Translational Bioinformatics (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:
Dumontier, M. (PI)
;
Gevaert, O. (PI)
;
Wall, D. (PI)
...
more instructors for BIOMEDIN 217 »
Instructors:
Dumontier, M. (PI)
;
Gevaert, O. (PI)
;
Wall, D. (PI)
;
Calderon, D. (TA)
;
Greenside, P. (TA)
;
Kim, D. (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. 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 at the level of
CS 106A; familiarity with statistics and biology.
Terms: Win
| Units: 2
BIOMEDIN 219: Mathematical Models and Medical Decisions
Analytic methods for determining the optimal diagnostic and therapeutic decisions for the care of individual patients and for the design of policies affecting the care of patient populations. Topics: utility theory and probability modeling, empirical methods for estimating disease prevalence, probability models for periodic processes, binary decision-making techniques, Markov models of dynamic disease state problems, utility assessment techniques, parametric utility models, utility models for multidimensional outcomes, analysis of time-varying clinical outcomes, and the design of cost-contstrained clinical policies. Extensive problem sets compliment course materials. Prerequisites: introduction to calculus and basic statistics.
Terms: Win
| Units: 2
Instructors:
Higgins, M. (PI)
;
Musen, M. (SI)
BIOMEDIN 224: Principles of Pharmacogenomics (GENE 224)
This course is an introduction to pharmacogenomics, including the relevant pharmacology, genomics, experimental methods (sequencing, expression, genotyping), data analysis methods and bioinformatics. The course reviews key gene classes (e.g., cytochromes, transporters) and key drugs (e.g., warfarin, clopidogrel, statins, cancer drugs) in the field. Resources for pharmacogenomics (e.g., PharmGKB, Drugbank, NCBI resources) are reviewed, as well as issues implementing pharmacogenomics testing in the clinical setting. Reading of key papers, including student presentations of this work; problem sets; final project selected with approval of instructor. Prerequisites: two of
BIO 41, 42, 43, 44X, 44Y or consent of instructor.
Terms: Win, Spr, Sum
| Units: 3
BIOMEDIN 225: Data Driven Medicine: Lectures
Lectures for
BIOMEDIN 215.With the spread of electronic health records and increasingly low cost assays for patient molecular data, powerful data repositories with tremendous potential for biomedical research, clinical care and personalized medicine are being built. But these databases are large and difficult for any one specialist to analyze. To find the hidden associations within the full set of data, we introduce methods for data-mining at the internet scale, the handling of large-scale electronic medical records data for machine learning, methods in natural language processing and text-mining applied to medical records, methods for using ontologies for the annotation and indexing of unstructured content as well as semantic web technologies. Prerequisites: familiarity with statistics (
STATS 202) and biology.
Terms: Aut
| Units: 2
BIOMEDIN 233: Intermediate Biostatistics: Analysis of Discrete Data (HRP 261, STATS 261)
Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
Terms: Win
| Units: 3
Instructors:
Sainani, K. (PI)
BIOMEDIN 245: Statistical and Machine Learning Methods for Genomics (BIO 268, CS 373, GENE 245, STATS 345)
Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Instruction includes lectures and discussion of readings from primary literature. Homework and projects require implementing some of the algorithms and using existing toolkits for analysis of genomic datasets.
Terms: Spr
| Units: 3
BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252)
Methods of conducting empirical studies which use large existing medical, survey, and other databases to ask both clinical and policy questions. Econometric and statistical models used to conduct medical outcomes research. How research is conducted on medical and health economics questions when a randomized trial is impossible. Problem sets emphasize hands-on data analysis and application of methods, including re-analyses of well-known studies. Prerequisites: one or more courses in probability, and statistics or biostatistics.
Terms: Spr
| Units: 4
Instructors:
Bendavid, E. (PI)
;
Bhattacharya, J. (PI)
BIOMEDIN 256: Economics of Health and Medical Care (BIOMEDIN 156, ECON 126, HRP 256)
Institutional, theoretical, and empirical analysis of the problems of health and medical care. Topics: demand for medical care and medical insurance; institutions in the health sector; economics of information applied to the market for health insurance and for health care; measurement and valuation of health; competition in health care delivery. Graduate students with research interests should take
ECON 249. Prerequisites:
ECON 50 and either
ECON 102A or
STATS 116 or the equivalent. Recommended:
ECON 51.
Terms: Aut
| Units: 5
Instructors:
Bhattacharya, J. (PI)
Filter Results: