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BIOMEDIN 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: Spr | Units: 5 | UG Reqs: WAY-SI

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, Sum | Units: 1 | Repeatable 3 times (up to 3 units total)
Instructors: ; Chaudhari, A. (PI); Daneshjou, R. (PI); Engelhardt, B. (PI); Matthys, K. (PI); Tian, L. (PI); Wall, D. (PI); Wong, W. (PI); Zou, J. (PI); Daneshjou, R. (SI); Sabatti, C. (SI); Witte, J. (SI)

BIOMEDIN 206: Informatics in Industry

Effective management, modeling, acquisition, and mining of biomedical information in healthcare and biotechnology companies and approaches to information management adopted by companies in this ecosystem. Guest speakers from pharmaceutical/biotechnology companies, clinics/hospitals, health communities/portals, instrumentation/software vendors. May be repeated for credit.
Terms: Spr | Units: 1 | Repeatable 12 times (up to 12 units total)
Instructors: ; Montgomery, S. (PI)

BIOMEDIN 208: Applied Clinical Informatics Seminar

Weekly seminar series in which seminal literature and current publications in the field of clinical informatics are reviewed and discussed. Organized by the Stanford Clinical Informatics fellowship program. Topics include electronic health record design, implementation, and evaluation; patient engagement; provider satisfaction; and hot topics in clinical informatics. Limited enrollment.
Terms: Spr | Units: 1 | Repeatable 2 times (up to 2 units total)
Instructors: ; Li, R. (PI); Morse, K. (PI)

BIOMEDIN 210: Modeling Biomedical Systems (CS 270)

At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools. Prerequisites: CS106A. Basic familiarity with Python programming, biology, probability, and logic are assumed.
Terms: Win, Spr | Units: 3
Instructors: ; Musen, M. (PI)

BIOMEDIN 212: Introduction to Biomedical Data Science Research Methodology (BIOE 212, CS 272, GENE 212)

Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended. Prerequisites: BIOMEDIN 210 or 214 or 215 or 217 or 260. Preference to BMI graduate students. Consent of instructor required.NOTE: For students in the Department of Biomedical Data Science Program, this core course MUST be taken as a letter grade only.
Terms: Spr | Units: 3-5
Instructors: ; Altman, R. (PI); Engelhardt, B. (SI); Matthys, K. (SI); Wall, D. (SI); Pi, S. (TA)

BIOMEDIN 217: Translational Bioinformatics (BIOE 217, CS 275, GENE 217)

Analytic 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; 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, Spr | Units: 3-4
Instructors: ; Plevritis, S. (PI); Enache, O. (TA); Lewis, A. (TA); Schuessler, M. (TA)

BIOMEDIN 222: Cloud Computing for Biology and Healthcare (CS 273C, GENE 222)

Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture. This class prepares students to understand how to design parallel programs for computationally intensive medical applications and how to run these applications on computing frameworks such as Cloud Computing and High Performance Computing (HPC) systems. Prerequisites: familiarity with programming in Python and R.
Terms: Spr | Units: 3
Instructors: ; Bahmani, A. (PI); Kundaje, A. (PI); Snyder, M. (PI)

BIOMEDIN 223: Deploying and Evaluating Fair AI in Healthcare (EPI 220)

AI applications are proliferating throughout the healthcare system and stakeholders are faced with the opportunities and challenges of deploying these quickly evolving technologies. This course teaches the principles of AI evaluations in healthcare, provides a framework for deployment of AI in the healthcare system, reviews the regulatory environment, and discusses fundamental components used to evaluate the downstream effects of AI healthcare solutions, including biases and fairness. Prerequisites: CS106A; familiarity with statistics (stats 202), BIOMED 215, or BIODS 220
Terms: Spr | Units: 2-3
Instructors: ; Hernandez-Boussard, T. (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
Instructors: ; Altman, R. (PI); Carrillo, M. (PI); Smith, D. (GP); Smith, D. (TA)

BIOMEDIN 225: Data Driven 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 class will teach you how to use EHRs and other patient data in conjunction with recent advances in artificial intelligence (AI) and evolving business models to improve healthcare. Upon completing this course, you should be able to: differentiate between and give examples of categories of care questions that AI can help answer, describe common healthcare data sources and their relative advantages, limitations, and biases in enabling care transformation, understand the challenges in using various kinds of clinical data to create fair algorithmic interventions, design an analysis of a clinical dataset, evaluate and criticize published research to separate hype from reality. Prerequisites: enrollment in the MCiM program. This course is designed to prepare you to pose and answer meaningful clinical questions using healthcare data as well as understand how AI can be brought into clinical use safely, ethically and cost-effectively.
Terms: Spr | Units: 3
Instructors: ; Shah, N. (PI)

BIOMEDIN 226: Digital Health Practicum in a Health Care Delivery System

Practical experience implementing clinical informatics solutions with a focus on digital health in one of the largest healthcare delivery systems in the United States. Individual meetings with senior clinical informatics leaders to discuss elements of successful projects. Implementation opportunities include supporting the use of electronic health records, engagement of patients and providers via a personal health record, use of informatics to support patient service centers, and improvement of patient access to clinical data. Consent of course instructors required at least one quarter prior to student enrollment in course.
Terms: Aut, Win, Spr | Units: 2-3
Instructors: ; Chan, A. (PI); Musen, M. (PI)

BIOMEDIN 251: Outcomes Analysis (HRP 252, MED 252)

This course introduces and develops methods for conducting empirical research that address clinical and policy questions that are not suitable for randomized trials. Conceptual and applied models of causal inference guide the design of empirical research. Econometric and statistical models are used to conduct health outcomes research which use large existing medical, survey, and other databases Problem sets emphasize hands-on data analysis and application of methods, including re-analyses of well-known studies. This is a project-based course designed for students pursuing research training. Prerequisites: one or more courses in probability, and statistics or biostatistics.
Terms: Spr | Units: 4
Instructors: ; Bendavid, E. (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; 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: Spr | Units: 5

BIOMEDIN 260: Computational Methods for Biomedical Image Analysis and Interpretation (BMP 260, CS 235, RAD 260)

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent. Prerequisites: programming ability at the level of CS 106A, familiarity with statistics, basic biology. Knowledge of Matlab or Python highly recommended.
Terms: Spr | Units: 3-4
Instructors: ; Rusu, M. (PI); Rubin, D. (SI); Bravo Sanchez, L. (TA); Franke, C. (TA); Lozano Garcia, E. (TA)

BIOMEDIN 290: Biomedical Informatics Teaching Methods

Hands-on training in biomedical informatics pedagogy. Practical experience in pedagogical approaches, variously including didactic, inquiry, project, team, case, field, and/or problem-based approaches. Students create course content, including lectures, exercises, and assessments, and evaluate learning activities and outcomes. Prerequisite: instructor consent.
Terms: Aut, Win, Spr, Sum | Units: 1-6 | Repeatable 2 times (up to 12 units total)
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bayati, M. (PI); Bejerano, G. (PI); Bhattacharya, J. (PI); Blish, C. (PI); Boahen, K. (PI); Brandeau, M. (PI); Bustamante, C. (PI); Butte, A. (PI); Chang, H. (PI); Cherry, J. (PI); Cohen, S. (PI); Covert, M. (PI); Curtis, C. (PI); Das, A. (PI); Das, R. (PI); Davis, R. (PI); Delp, S. (PI); Desai, M. (PI); Dill, D. (PI); Dumontier, M. (PI); Elias, J. (PI); Fagan, L. (PI); Feldman, M. (PI); Ferrell, J. (PI); Fraser, H. (PI); Gerritsen, M. (PI); Gevaert, O. (PI); Goldstein, M. (PI); Greenleaf, W. (PI); Guibas, L. (PI); Hastie, T. (PI); Hlatky, M. (PI); Holmes, S. (PI); Ji, H. (PI); Karp, P. (PI); Khatri, P. (PI); Kim, S. (PI); Kirkegaard, K. (PI); Klein, T. (PI); Koller, D. (PI); Krummel, T. (PI); Kundaje, A. (PI); Levitt, M. (PI); Levitt, R. (PI); Li, J. (PI); Longhurst, C. (PI); Lowe, H. (PI); Mallick, P. (PI); Manning, C. (PI); McAdams, H. (PI); Menon, V. (PI); Montgomery, S. (PI); Musen, M. (PI); Napel, S. (PI); Nolan, G. (PI); Olshen, R. (PI); Owen, A. (PI); Owens, D. (PI); Paik, D. (PI); Palacios, J. (PI); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Relman, D. (PI); Rivas, M. (PI); Rubin, D. (PI); Sabatti, C. (PI); Salzman, J. (PI); Shachter, R. (PI); Shafer, R. (PI); Shah, N. (PI); Sherlock, G. (PI); Sidow, A. (PI); Snyder, M. (PI); Tang, H. (PI); Taylor, C. (PI); Tibshirani, R. (PI); Utz, P. (PI); Walker, M. (PI); Wall, D. (PI); Winograd, T. (PI); Wong, W. (PI); Xing, L. (PI); Zou, J. (PI); Choudhry, S. (GP)

BIOMEDIN 299: Directed Reading and Research

For students wishing to receive credit for directed reading or research time. Prerequisite: consent of instructor. (Staff)
Terms: Aut, Win, Spr, Sum | Units: 1-18 | Repeatable for credit
Instructors: ; Aghaeepour, N. (PI); Alarid Escudero, F. (PI); Altman, R. (PI); Ashley, E. (PI); Baiocchi, M. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bayati, M. (PI); Bejerano, G. (PI); Bendavid, E. (PI); Bhattacharya, J. (PI); Blish, C. (PI); Boahen, K. (PI); Brandeau, M. (PI); Bustamante, C. (PI); Butte, A. (PI); Chang, H. (PI); Chaudhari, A. (PI); Chen, J. (PI); Cherry, J. (PI); Cohen, S. (PI); Covert, M. (PI); Das, A. (PI); Das, R. (PI); Davis, R. (PI); Delp, S. (PI); Desai, M. (PI); Dill, D. (PI); Dror, R. (PI); Dumontier, M. (PI); Elias, J. (PI); Engelhardt, B. (PI); Fagan, L. (PI); Feldman, M. (PI); Ferrell, J. (PI); Fraser, H. (PI); Gentles, A. (PI); Gerritsen, M. (PI); Gevaert, O. (PI); Goldstein, M. (PI); Greenleaf, W. (PI); Guibas, L. (PI); Hastie, T. (PI); Hernandez-Boussard, T. (PI); Hlatky, M. (PI); Holmes, S. (PI); Jerby, L. (PI); Ji, H. (PI); Karp, P. (PI); Khatri, P. (PI); Kim, S. (PI); Kirkegaard, K. (PI); Klein, T. (PI); Koller, D. (PI); Krummel, T. (PI); Kundaje, A. (PI); Langlotz, C. (PI); Leskovec, J. (PI); Levitt, M. (PI); Li, J. (PI); Longhurst, C. (PI); Lowe, H. (PI); Lu, Y. (PI); Mallick, P. (PI); Manning, C. (PI); McAdams, H. (PI); Menon, V. (PI); Montgomery, S. (PI); Musen, M. (PI); Napel, S. (PI); Nolan, G. (PI); Owen, A. (PI); Owens, D. (PI); Paik, D. (PI); Palacios, J. (PI); Palaniappan, L. (PI); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Pohl, K. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Relman, D. (PI); Rivas, M. (PI); Rose, S. (PI); Ross, E. (PI); Rubin, D. (PI); Sabatti, C. (PI); Salzman, J. (PI); Shachter, R. (PI); Shafer, R. (PI); Shah, N. (PI); Sherlock, G. (PI); Sidow, A. (PI); Snyder, M. (PI); Tang, H. (PI); Taylor, C. (PI); Tibshirani, R. (PI); Tu, S. (PI); Utz, P. (PI); Walker, M. (PI); Wall, D. (PI); Winograd, T. (PI); Witte, J. (PI); Wong, W. (PI); Xing, L. (PI); Yeung, S. (PI); Zou, J. (PI)

BIOMEDIN 370: Medical Scholars Research

Provides an opportunity for student and faculty interaction, as well as academic credit and financial support, to medical students who undertake original research. Enrollment is limited to students with approved projects.
Terms: Aut, Win, Spr, Sum | Units: 4-18 | Repeatable for credit
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bayati, M. (PI); Bejerano, G. (PI); Bhattacharya, J. (PI); Blish, C. (PI); Boahen, K. (PI); Brandeau, M. (PI); Bustamante, C. (PI); Butte, A. (PI); Chang, H. (PI); Chen, J. (PI); Cherry, J. (PI); Cohen, S. (PI); Covert, M. (PI); Das, A. (PI); Das, R. (PI); Davis, R. (PI); Delp, S. (PI); Desai, M. (PI); Dill, D. (PI); Dror, R. (PI); Dumontier, M. (PI); Elias, J. (PI); Fagan, L. (PI); Feldman, M. (PI); Ferrell, J. (PI); Fraser, H. (PI); Gerritsen, M. (PI); Gevaert, O. (PI); Goldstein, M. (PI); Greenleaf, W. (PI); Guibas, L. (PI); Hastie, T. (PI); Hlatky, M. (PI); Holmes, S. (PI); Ji, H. (PI); Karp, P. (PI); Khatri, P. (PI); Kim, S. (PI); Kirkegaard, K. (PI); Klein, T. (PI); Koller, D. (PI); Krummel, T. (PI); Kundaje, A. (PI); Levitt, M. (PI); Li, J. (PI); Longhurst, C. (PI); Lowe, H. (PI); Mallick, P. (PI); Manning, C. (PI); McAdams, H. (PI); Menon, V. (PI); Montgomery, S. (PI); Musen, M. (PI); Napel, S. (PI); Nolan, G. (PI); Owen, A. (PI); Owens, D. (PI); Paik, D. (PI); Palacios, J. (PI); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Relman, D. (PI); Rivas, M. (PI); Rubin, D. (PI); Sabatti, C. (PI); Salomon, J. (PI); Salzman, J. (PI); Shachter, R. (PI); Shafer, R. (PI); Shah, N. (PI); Sherlock, G. (PI); Sidow, A. (PI); Snyder, M. (PI); Tang, H. (PI); Taylor, C. (PI); Tibshirani, R. (PI); Tu, S. (PI); Utz, P. (PI); Walker, M. (PI); Wall, D. (PI); Winograd, T. (PI); Wong, W. (PI); Xing, L. (PI); Zou, J. (PI)

BIOMEDIN 390A: Curricular Practical Training

Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any follow-up on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: ; Musen, M. (PI); Tian, L. (PI)

BIOMEDIN 390B: Curricular Practical Training

Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any follow-up on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: ; Musen, M. (PI); Tian, L. (PI)

BIOMEDIN 390C: Curricular Practical Training

Provides educational opportunities in biomedical informatics research. Qualified biomedical informatics students engage in internship work and integrate that work into their academic program. Students register during the quarter they are employed and must complete a research report outlining their work activity, problems investigated, key results, and any follow-up on projects they expect to perform. BIOMEDIN 390A, B, and C may each be taken only once.
Terms: Aut, Win, Spr, Sum | Units: 1
Instructors: ; Musen, M. (PI); Tian, L. (PI)

BIOMEDIN 801: TGR Master's Project

Project credit for masters students who have completed all course requirements and minimum of 45 Stanford units.
Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
Instructors: ; Altman, R. (PI); Ashley, E. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bayati, M. (PI); Bejerano, G. (PI); Bhattacharya, J. (PI); Blish, C. (PI); Boahen, K. (PI); Brandeau, M. (PI); Bustamante, C. (PI); Butte, A. (PI); Chang, H. (PI); Cherry, J. (PI); Cohen, S. (PI); Covert, M. (PI); Das, A. (PI); Das, R. (PI); Davis, R. (PI); Delp, S. (PI); Desai, M. (PI); Dill, D. (PI); Dumontier, M. (PI); Elias, J. (PI); Fagan, L. (PI); Feldman, M. (PI); Ferrell, J. (PI); Fraser, H. (PI); Gentles, A. (PI); Gerritsen, M. (PI); Gevaert, O. (PI); Goldstein, M. (PI); Greenleaf, W. (PI); Guibas, L. (PI); Hastie, T. (PI); Hlatky, M. (PI); Holmes, S. (PI); Ji, H. (PI); Karp, P. (PI); Khatri, P. (PI); Kim, S. (PI); Kirkegaard, K. (PI); Klein, T. (PI); Koller, D. (PI); Krummel, T. (PI); Kundaje, A. (PI); Levitt, M. (PI); Li, J. (PI); Longhurst, C. (PI); Lowe, H. (PI); Lu, Y. (PI); Mallick, P. (PI); Manning, C. (PI); McAdams, H. (PI); Menon, V. (PI); Montgomery, S. (PI); Musen, M. (PI); Napel, S. (PI); Nolan, G. (PI); Owen, A. (PI); Owens, D. (PI); Paik, D. (PI); Palacios, J. (PI); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Relman, D. (PI); Rivas, M. (PI); Rubin, D. (PI); Sabatti, C. (PI); Salzman, J. (PI); Shachter, R. (PI); Shafer, R. (PI); Shah, N. (PI); Sherlock, G. (PI); Sidow, A. (PI); Snyder, M. (PI); Tang, H. (PI); Taylor, C. (PI); Tian, L. (PI); Tibshirani, R. (PI); Tu, S. (PI); Utz, P. (PI); Walker, M. (PI); Wall, D. (PI); Winograd, T. (PI); Wong, W. (PI); Xing, L. (PI); Zou, J. (PI)

BIOMEDIN 802: TGR PhD Dissertation

Terms: Aut, Win, Spr, Sum | Units: 0 | Repeatable for credit
Instructors: ; Aghaeepour, N. (PI); Altman, R. (PI); Ashley, E. (PI); Baiocchi, M. (PI); Bassik, M. (PI); Batzoglou, S. (PI); Bayati, M. (PI); Bejerano, G. (PI); Bhattacharya, J. (PI); Blish, C. (PI); Boahen, K. (PI); Brandeau, M. (PI); Bustamante, C. (PI); Butte, A. (PI); Chang, H. (PI); Chaudhari, A. (PI); Cherry, J. (PI); Cohen, S. (PI); Covert, M. (PI); Das, A. (PI); Das, R. (PI); Davis, R. (PI); Delp, S. (PI); Desai, M. (PI); Dill, D. (PI); Dumontier, M. (PI); Elias, J. (PI); Fagan, L. (PI); Feldman, M. (PI); Ferrell, J. (PI); Fraser, H. (PI); Gerritsen, M. (PI); Gevaert, O. (PI); Goldstein, M. (PI); Greenleaf, W. (PI); Guibas, L. (PI); Hastie, T. (PI); Hlatky, M. (PI); Holmes, S. (PI); Ji, H. (PI); Karp, P. (PI); Khatri, P. (PI); Kim, S. (PI); Kirkegaard, K. (PI); Klein, T. (PI); Koller, D. (PI); Krummel, T. (PI); Kundaje, A. (PI); Leskovec, J. (PI); Levitt, M. (PI); Li, J. (PI); Longhurst, C. (PI); Lowe, H. (PI); Mallick, P. (PI); Manning, C. (PI); McAdams, H. (PI); Menon, V. (PI); Montgomery, S. (PI); Musen, M. (PI); Napel, S. (PI); Nolan, G. (PI); Owen, A. (PI); Owens, D. (PI); Paik, D. (PI); Palacios, J. (PI); Pande, V. (PI); Petrov, D. (PI); Plevritis, S. (PI); Poldrack, R. (PI); Pritchard, J. (PI); Rehkopf, D. (PI); Relman, D. (PI); Rivas, M. (PI); Rubin, D. (PI); Sabatti, C. (PI); Salzman, J. (PI); Shachter, R. (PI); Shafer, R. (PI); Shah, N. (PI); Sherlock, G. (PI); Sidow, A. (PI); Snyder, M. (PI); Tang, H. (PI); Taylor, C. (PI); Tian, L. (PI); Tibshirani, R. (PI); Tu, S. (PI); Utz, P. (PI); Walker, M. (PI); Wall, D. (PI); Winograd, T. (PI); Wong, W. (PI); Xing, L. (PI); Yeung, S. (PI); Zou, J. (PI)
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