printer friendly pageBIOMEDIN 156: Economics of Health and Medical Care (BIOMEDIN 256, 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; economics of health care labor markets and health care production; and economic epidemiology. 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
| UG Reqs: WAY-SI
Instructors:
Bhattacharya, J. (PI)
;
MaCurdy, T. (PI)
BIOMEDIN 173A: Foundations of Computational Human Genomics (CS 173A, DBIO 173A)
(Only one of 173A or 273A counts toward any CS degree program.) A coder's primer to Computational Biology through the most amazing "source code" known: your genome. Examine the major forces of genome "code development" - positive, negative and neutral selection. Learn about genome sequencing (discovering your source code from fragments); genome content: variables (genes), control-flow (gene regulation), run-time stacks (epigenomics) and memory leaks (repeats); personalized genomics and genetic disease (code bugs); genome editing (code injection); ultra conservation (unsolved mysteries) and code modifications behind amazing animal adaptations. Course includes primers on molecular biology and text processing. Prerequisites: comfortable coding in Python from the command line.
Terms: Aut
| Units: 3-4
Instructors:
Bejerano, G. (PI)
;
Chen, Z. (TA)
BIOMEDIN 201: Biomedical Informatics Student Seminar (BIODS 201)
Participants report on recent articles from the Biomedical Informatics literature or their research projects. Goals are to teach critical reading of scientific papers and presentation skills. Summer Quarter consists of critical review of relevant literature led by faculty associated with the Biomedical Informatics Program. May be repeated three times for credit.
Terms: Aut, Win, Spr
| Units: 1
| Repeatable
3 times
(up to 3 units total)
Instructors:
Engelhardt, B. (PI)
;
Lu, Y. (PI)
;
Matthys, K. (PI)
...
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Instructors:
Engelhardt, B. (PI)
;
Lu, Y. (PI)
;
Matthys, K. (PI)
;
Sabatti, C. (PI)
;
Tian, L. (PI)
;
Wall, D. (PI)
;
Wong, W. (PI)
BIOMEDIN 205: Precision Practice with Big Data
Primarily for M.D. students; open to other graduate students. Provides an overview of how to leverage large amounts of clinical, molecular, and imaging data within hospitals and in cyberspace--big data--to practice medicine more effectively. Lectures by physicians, researchers, and industry leaders survey how the major methods of informatics can help physicians leverage big data to profile disease, to personalize treatment to patients, to predict treatment response, to discover new knowledge, and to challenge established medical dogma and the current paradigm of clinical decision-making based solely on published knowledge and individual physician experience. Prerequisite: background in biomedicine. Background in computer science can be helpful but not required.
Terms: Aut
| Units: 1
Instructors:
Chen, J. (PI)
;
Maddali, M. (TA)
BIOMEDIN 214: Representations and Algorithms for Computational Molecular Biology (BIOE 214, CS 274, GENE 214)
Topics: This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, computing with strings, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D biological data, integration of data sources, knowledge representation and controlled terminologies for molecular biology, microarray analysis, chemoinformatics, pharmacogenetics, network biology. Note: For Fall 2021, Dr. Altman will be away on sabbatical and so class will be taught from lecture videos recorded in fall of 2018. The class will be entirely online, with no scheduled meeting times. Lectures will be released in batches to encourage pacing. A team of TAs will manage all class logistics and grading. Firm prerequisite:
CS 106B.
Terms: Aut
| Units: 3-4
BIOMEDIN 215: Data Science for Medicine
The widespread adoption of electronic health records (EHRs) has created a new source of big data namely, the record of routine clinical practice as a by-product of care. This graduate class will teach you how to use EHRs and other patient data to discover new clinical knowledge and improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of research questions and the study designs used to address them, describe common healthcare data sources and their relative advantages and limitations, extract and transform various kinds of clinical data to create analysis-ready datasets, design and execute an analysis of a clinical dataset based on your familiarity with the workings, applicability, and limitations of common statistical methods, evaluate and criticize published research using your knowledge of 1-4 to generate new research ideas and separate hype from reality. Prerequisites:
CS 106A or equivalent,
STATS 60 or equivalent. Recommended:
STATS 216,
CS 145, STATS 305NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Aut
| Units: 3
Instructors:
Shah, N. (PI)
;
Bedi, S. (TA)
;
Hassan, S. (TA)
...
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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 221: Machine Learning Approaches for Data Fusion in Biomedicine (BIODS 221)
Vast amounts of biomedical data are now routinely available for patients, raging from genomic data, to radiographic images and electronic health records. AI and machine learning are increasingly used to enable pattern discover to link such data for improvements in patient diagnosis, prognosis and tailoring treatment response. Yet, few studies focus on how to link different types of biomedical data in synergistic ways, and to develop data fusion approaches for improved biomedical decision support. This course will describe approaches for multi-omics, multi-modal and multi-scale data fusion of biomedical data in the context of biomedical decision support. Prerequisites: CS106A or equivalent,
Stats 60 or equivalent.
Terms: Aut
| Units: 2-3
Instructors:
Gentles, A. (PI)
;
Gevaert, O. (PI)
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: Aut, Spr
| Units: 3
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; economics of health care labor markets and health care production; and economic epidemiology. 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)
;
MaCurdy, T. (PI)
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