Computational Biology and Biomedical Informatics
100 College St., cbb-registrar@yale.edu
http://cbb.yale.edu
M.S., Ph.D.
Directors of Graduate Studies
Mark Gerstein (Bass 432A, 203.432.6105, cbb-dgs@yale.edu)
Steven Kleinstein (300 George St., Suite 505, 203.785.6685, cbb-dgs@yale.edu)
Professors Frederick Altice (Internal Medicine; Infections Diseases; Epidemiology of Microbial Diseases), Marcus Bosenberg (Dermatology; Pathology), Cynthia Brandt (Emergency Medicine; Anesthesiology), Joseph Chang (Statistics and Data Science), Kei-Hoi Cheung (Emergency Medicine; Anesthesiology), Ronald Coifman (Mathematics; Computer Science), Stephen Dellaporta (Molecular, Cellular, and Developmental Biology), Rong Fan (Biomedical Engineering; Pathology), Richard Flavell (Immunobiology), Joel Gelernter (Psychiatry; Genetics), Mark Gerstein (Biomedical Informatics; Molecular Biophysics and Biochemistry; Computer Science; Statistics and Data Science), Antonio Giraldez (Genetics), Jeffrey Gruen (Genetics; Investigative Medicine; Pediatrics), Murat Gunel (Neurosurgery; Genetics), Ira Hall (Genetics), Amy Justice (Internal Medicine; Public Health), Naftali Kaminski (Internal Medicine), Steven Kleinstein (Pathology; Immunobiology), Yuval Kluger (Pathology), Harlan Krumholz (Internal Medicine; Investigative Medicine; Public Health), Haifan Lin (Cell Biology; Genetics), Shuangge (Steven) Ma (Biostatistics), Zongming Ma (Statistics and Data Science), Andrew Miranker (Molecular Biophysics and Biochemistry; Chemical and Environmental Engineering), James Noonan (Genetics; Neuroscience), Corey O’Hern (Mechanical Engineering and Materials Science; Applied Physics; Physics), Xenophon Papademetris (Biomedical Informatics and Data Science; Radiology and Biomedical Imaging), Lajos Pusztai (Internal Medicine), Anna Pyle (Molecular, Cellular, and Developmental Biology; Chemistry), David Stern (Pathology), Hemant Tagare (Radiology and Biomedical Imaging; Biomedical Engineering), Jeffrey Townsend (Public Health; Ecology and Evolutionary Biology), John Tsang (Immunobiology), Hua Xu (Biomedical Informatics and Data Science), Heping Zhang (Biostatistics; Statistics and Data Science), Hongyu Zhao (Biostatistics; Statistics and Data Science), Steven Zucker (Computer Science; Electrical Engineering; Biomedical Engineering)
Associate Professors Julien Berro (Molecular Biophysics and Biochemistry), Sidi Chen (Genetics; Neurosurgery), Forrest Crawford (Biostatistics; Ecology and Evolutionary Biology), Samah Jarad (Emergency Medicine; Biostatistics), Smita Krishnaswamy (Genetics; Computer Science), Bluma Lesch (Genetics), Jun Lu (Genetics), Ted Melnick (Biostatistics; Emergency Medicine), Kathryn Miller-Jensen (Engineering and Applied Science), John Murray (Psychiatry; Neuroscience; Physics), Renato Polimanti (Psychiatry), Edward Stites (Laboratory Medicine), Andrew Taylor (Emergency Medicine), Zuoheng (Anita) Wang (Biostatistics), Yize Zhao (Biostatistics)
Assistant Professors Arnaud Augert (Pathology), David Braun (Medical Oncology), Purushottam Dixit (Biomedical Engineering), Salil Garg (Laboratory Medicine; Pathology), Leying Guan (Biostatistics), Mary-Anne Hartley (Biomedical Informatics and Data Science), Albert Higgins-Chen (Psychiatry; Pathology), Jeffrey Ishizuka (Internal Medicine; Medical Oncology; Pathology), Rohan Khera (Internal Medicine, Cardiovascular Medicine; EPH Biostatistics), Monkol Lek (Genetics), Benjamin Machta (Physics), Robert McDougal (Biostatistics), Jacob Musser (Molecular, Cellular, and Developmental Biology), C. Brandon Ogbunu (Ecology and Evolutionary Biology), Carlos Oliveira (Pediatrics; Infectious Diseases), Steven Reilly (Genetics), Wade Schulz (Laboratory Medicine), Serena Tucci (Anthropology), David van Dijk (Internal Medicine, Cardiology; Computer Science), Rex Ying (Computer Science), Jack Zhang (Molecular Biophysics and Biochemistry)
Fields of Study
Computational biology and biomedical informatics (CB&B) is a rapidly developing multidisciplinary field. The past two decades have witnessed a revolution in the biological and biomedical sciences driven by the development of technologies such as high-dimensional phenotypic profiling, next-generation sequencing, macromolecular structure determination and high-resolution imaging, wearable sensor devices, and large-scale electronic health records. These data-generation technologies demand new computational analysis approaches, which, in turn, have given rise to the field of computational biology and biomedical informatics (CB&B).
The Yale Computational Biology and Biomedical Informatics program combines research training opportunities in a range of different fields within the biological and biomedical sciences, in addition to the computational sciences, applied mathematics, statistics, and data science. The scope and balance of a student's program are highly individualized. Each student in the CB&B program develops, with the assistance of faculty advisers, a specific program of coursework, independent reading, and research that gives a depth of coverage and fits their background, interests, and career goals.
To enter the Ph.D. program, students apply to the CB&B Track within the interdepartmental graduate program in Biological and Biomedical Sciences (BBS), https://medicine.yale.edu/bbs.
Integrated Graduate Program in Physical and Engineering Biology (PEB)
Students applying to one of the tracks of the Biological and Biomedical Sciences program may simultaneously apply to be part of the PEB program. See the description under Non-Degree-Granting Programs, Councils, and Research Institutes for course requirements, and http://peb.yale.edu for more information about the benefits of this program and application instructions.
Special Requirements for the Ph.D. Degree
With the help of a faculty advisory committee, each student plans a program that includes courses, seminars, laboratory rotations, and independent reading. Students are expected to gain competence in three core areas: (1) computational biology and biomedical informatics, (2) biological sciences, and (3) informatics (including computer science, applied mathematics, statistics, and data science). While the courses taken to satisfy the core areas of competency may vary considerably, all students are required to take the following courses: CB&B 7400 and CB&B 7520 . CB&B requires a minimum of ten course credits. Completion of the core curriculum will typically take three to four terms, depending in part on the prior training of the student. With approval of the CB&B director of graduate studies (DGS), students may take one or two undergraduate courses to satisfy areas of minimum expected competency. Students will typically take two to three courses each term and three research rotations (CB&B 7110, CB&B 7120, CB&B 7130 ) during the first year. In addition to all other requirements, students must successfully complete IBIO 6010, Fundamentals of Research: Responsible Conduct of Research, (or another course that covers the material) prior to the end of their first year of study. After the first year, students will start working in the laboratory of their Ph.D. thesis supervisor. Students must pass a qualifying examination normally given no later than the end of the third year. There is no foreign language requirement. Students will serve as teaching assistants in two terms. In their fourth year of study, all students must successfully complete CB&B 5030 , RCR Refresher Course.
M.D.-Ph.D. Students
Students pursuing the joint M.D.-Ph.D. degrees must satisfy the course requirements listed above for Ph.D. students. With approval of the DGS, some courses taken toward the M.D. degree can be counted toward the ten required course credits. Such courses must have a graduate course number, and the student must register for them as graduate courses (in which grades are received). Laboratory rotations are available but not required. One teaching assistantship is required.
Master’s Degree
Terminal Master’s Degree Program Students can be admitted for a terminal M.S. degree in Computational Biology and Biomedical Informatics with the goal of gaining competency in three core areas: (1) computational biology and biomedical informatics, (2) biological and medical sciences, and (3) informatics (including computer science, applied mathematics, statistics, and data science). This is a two-year program and is not part of the BBS program. Students must complete twelve courses at Yale, including at least four graduate CB&B courses (including CB&B 7400 and CB&B 7520), two graduate courses in the biological and medical sciences, three graduate courses in areas of informatics, and three additional courses in any of the three core areas. In addition, M.S. students must take a one-term graduate seminar on research ethics and attend a CB&B seminar series. Finally, students must meet all of the graduate school’s requirements for the two-year terminal M.S. degree. We also discourage auditing courses, which do not satisfy the degree requirements.
Terminal M.S. degree students are also expected to complete an M.S. project, write a research paper describing it, and defend the project in a seminar where they present the project and answer questions about the project as well as demonstrate breadth knowledge of their coursework and track of study. The paper is evaluated by the student’s research supervisor and a second reader from the CB&B faculty. Students are expected to identify a faculty member to supervise the M.S. project by the end of the first year or early in the second year. Completion of the research paper is facilitated by enrolling in CB&B 7140 and CB&B 7150.
M.S. (en route to the Ph.D.) Students enrolled in the Ph.D. program may be awarded an M.S. degree en route as they satisfy the requirements for the Ph.D. degree. To qualify for the awarding of the en route M.S. degree a student must (1) complete two years (four terms) of study in the Ph.D. program; (2) complete the required coursework for the Ph.D. program, with ten required course credits taken at Yale including three successful research rotations; and (3) meet the graduate school’s grade requirements.
CB&B 5230b / ENAS 5410b / MB&B 5230b / MSCI 5410b / PHYS 5230b, Biological Physics Yimin Luo
This course has three aims: (1) to introduce students to the physics of biological systems, (2) to introduce students to the basics of scientific computing, and (3) to familiarize students with characterization methods and analysis tools. We focus on studies of a broad range of biophysical phenomena including diffusion, polymer statistics, entropic forces, membranes, and cell motion using computational tools and methods. We provide intensive tutorials for Matlab including basic syntax, arrays, functions, plotting, and importing and exporting data.
TTh 2:35pm-3:50pm
CB&B 5620b / ENAS 5620b / INP 7562b / MB&B 5620b / MCDB 5620b / PHYS 5620b, Modeling Biological Systems II Thierry Emonet and Harry McNamara
This course covers advanced topics in computational biology. How do cells compute, how do they count and tell time, how do they oscillate and generate spatial patterns? Topics include time-dependent dynamics in regulatory, signal-transduction, and neuronal networks; fluctuations, growth, and form; mechanics of cell shape and motion; spatially heterogeneous processes; diffusion. This year, the course spends roughly half its time on mechanical systems at the cellular and tissue level, and half on models of neurons and neural systems in computational neuroscience. Prerequisite: a 2000-level biology course or permission of the instructor.
TTh 2:35pm-3:50pm
CB&B 5710a, Data Science Grant-Writing Practicum Lucila Ohno-Machado
This is a hands-on course where students review funded and non-funded grant proposals for different types of NIH awards, as well as the critiques provided by the reviewers. Proposals in informatics and data science are different than traditional basic sciences proposals and clinical research proposals, so this course is specific for those proposing data science and informatics innovation that can be applied in biology and/or medicine. Although there is an emphasis on K (mentored) and F awards, we also cover the basics of R (non-mentored) research awards. Instructors and classmates review proposals that students prepare as part of the course.
M 10am-11am
CB&B 5800a, Bioinformatics Algorithms in Genomics Haoyu Cheng
This course introduces key algorithms used in computational genomics, with a focus on both classical bioinformatics methods and emerging machine learning and deep learning approaches. Topics covered include sequence alignment, genome assembly and comparative genomics, variant identification and analysis, and gene expression and regulation, along with advanced techniques for specialized applications such as cancer genomics. Through hands-on exercises and projects, students gain practical experience in implementing algorithms and analyzing real-world genomic data. By the end of the course, students are prepared to conduct independent genomic analyses or develop novel bioinformatics algorithms to tackle emerging challenges in genomics.
W 9:30am-12pm
CB&B 5802a, Applied Clinical and Operational Informatics Allen Hsiao and Menaka Sarav
This advanced course prepares students to understand how to apply informatics in real-world clinical and operational environments. Bridging the gap between theory and practice, the course covers the design, implementation, evaluation, and governance of informatics solutions in academic health systems. Led by practicing physician-informaticians, research and operational leaders, students learn the importance of understanding clinician workflows, analyze real case studies, engage with operational leaders, and evaluate interventions using current EHR tools, digital health technologies, artificial intelligence applications, and data platforms.
T 9am-11:30am
CB&B 6340a, Computational Methods for Informatics Staff
This course introduces the key computational methods and concepts necessary for taking an informatics project from start to finish: using APIs to query online resources, reading and writing common biomedical data formats, choosing appropriate data structures for storing and manipulating data, implementing computationally efficient and parallelizable algorithms for analyzing data, and developing appropriate visualizations for communicating health information. The FAIR data-sharing guidelines are discussed. Current issues in big health data are discussed, including successful applications as well as privacy and bias concerns. This course has a significant programming component, and familiarity with programming is assumed. Prerequisite: CPSC 223 or equivalent, or permission of the instructor.
HTBA
CB&B 6500a / GENE 6500a, Quantitative Foundations for Human Genetics Hoon Cho and Steven Reilly
This course provides an in-depth, comprehensive foundation in the genetic concepts and statistical methods required to analyze human genetic data. Quantitative frameworks including statistical modeling, inference, and machine learning approaches are taught in the context of human genetics and genomics, with applications spanning population and evolutionary genetics, complex trait modeling, genomic architecture and linkage, genome-wide association studies, and disease risk prediction. Through interactive discussions, primary literature review, and hands-on workshops, students learn to analyze and interpret quantitative, genetic data and to apply computational approaches for statistical analyses. This course is intended for graduate students who plan to use quantitative and statistical methods in their thesis research or who seek deeper knowledge in these areas. Prior experience with basic probability and Python coding is recommended; additional training will be provided to students who have limited background. Permission of the instructor is required. Interested students must contact the instructor to discuss their prior experience and expectations for the course.
F 9:30am-11am, MW 1pm-2pm
CB&B 6550a / GENE 6550a, Stem Cells: Biology and Application In-Hyun Park
This course is designed for first-year or second-year students to learn the fundamentals of stem cell biology and to gain familiarity with current research in the field. The course is presented in a lecture and discussion format based on primary literature. Topics include stem cell concepts, methodologies for stem cell research, embryonic stem cells, adult stem cells, cloning and stem cell reprogramming, and clinical applications of stem cell research. Prerequisites: undergraduate-level cell biology, molecular biology, and genetics.
Th 1:30pm-3pm
CB&B 7110a, Lab Rotations Steven Kleinstein
Three 2.5–3-month research rotations in faculty laboratories are required during the first year of graduate study. These rotations are arranged by each student with individual faculty members.
HTBA
CB&B 7140a, Research Paper in Computational Biology and Biomedical Informatics Anthony Lisi and Hua Xu
This two-semester single credit pass/fail course must be completed as part of the terminal M.S. degree program in computational biology and biomedical informatics (CB&B). Students work with a faculty supervisor in designing their project and writing their research paper. The syllabus details the intended scope and process for writing the research paper. In the broadest terms, the research paper must be of publishable quality and defensible in a public scientific forum. The student’s research supervisor is responsible for managing the intended product. The preferred format of the research paper for students is one that is in the style and length of a publishable, peer-reviewed paper, templated based on the journal submission. Prerequisite: second-year enrollment in program.
HTBA
CB&B 7141a / SOCY 7048a, AI, Medicine, and Society Alka Menon and Xenophon Papademetris
AI has shown tremendous promise to address problems in medicine and science. There is also considerable hype surrounding AI and many concerns (some justified, some not) regarding the use of this type of technology. This discussion-based seminar (1) provides undergraduate students across disciplines with a broad overview of issues related to AI in medicine at a non-technical level, drawing on perspectives from the interpretive/humanistic social sciences, computing, engineering, and healthcare and (2) models interdisciplinary communication and build a robust framework for collaboration. Overarching topics, grounded in medical case studies, include what it means for computers to “think” and how we understand what they are thinking about; the use and limits of scientific knowledge in making policy decisions; bias, fairness, equity, equality; the challenges of implementation of AI systems; safety and risk; and the human/computer interface. The course also provides a high level overview of machine learning, discussing opportunities, limitations, and tradeoffs. Ultimately, the course offers a grounded look at how AI is being discussed and deployed on the ground in medicine, equipping students with a critical lens for thinking about responsible and practical implementation and innovation when it comes to AI.
TTh 2:35pm-3:50pm
CB&B 7400a, Introduction to Health Informatics Ting-Ting Kuo and Andrew Loza
The course provides an introduction to clinical and translational informatics. Topics include (1) overview of biomedical informatics, (2) design, function, and evaluation of clinical information systems, (3) clinical decision-making and practice guidelines, (4) clinical decision support systems, (5) informatics support of clinical research, (6) privacy and confidentiality of clinical data, (7) standards, and (8) topics in translational bioinformatics. Permission of the instructor required.
TTh 11:35am-12:50pm