Courses

The Biomedical Informatics curriculum is designed to provide a uniform foundation in the essentials of the field while meeting the needs of a wide range of students with different backgrounds and career goals. The educational objectives consist of core courses, which provide a foundation in general Biomedical Informatics methods, techniques, and theories. The qualitative, quantitative, information technology objectives enable students to apply these methods to one or more domain of specialization, which includes data science, clinical informatics, translational informatics, bioinformatics, or public health informatics.

Students must demonstrate competence in areas that serve as a building block for Biomedical Informatics by successfully completing relevant graduate level courses. A number of Biomedical Informatics courses are offered to meet the educational objectives. Students are also permitted to take courses to meet educational objective and domain requirements that are not listed below with approval from their research or academic advisors or the graduate program director.

Examples of Objective-Domain Trajectories
PhDs and Postdocs:

Data Science: 2 Quant Objectives + 1 IT Objective + 2 Domain Courses
Clinical/Public Health: 3 Qual, Quant, or IT Objectives + 2 Domain Courses (at least 1 should be Quant or IT) + 2 Domain Courses
– Bioinformatics: 2 Quant Objectives + 1 IT Objective + 2 Domain Courses
Translational: 2 Quant Objectives + 1 IT Objective + 2 Domain Courses

MAs:
Data Science: 2 Quant Objectives + 2 Domain Courses
Clinical/Public Health: 2 Qual, Quant, or IT Objectives (at least one should be Quant or IT) + 2 Domain Courses
Bioinformatics: 2 Qual, Quant, or IT Objectives + 2 Domain Courses
Translational: 2 Qual, Quant, or IT Objectives + 2 Domain Courses

Credits
PhD students may enroll in up to 20 credits per term without incurring additional charges. Any points in excess of 20 are not covered by the department, but may be covered by the student’s research advisor with prior approval before enrolling.  Depending upon degree type, in addition to the credit load from the core, objectives, and domain courses, students may also need to register for Research Projects (BINF G6001, BINF G9001 and BINF G9999), the Ethics Course (CMBS G4010), the MPhil Course (BINF G8010), or Research Seminar (BINF G4099). More information on these courses can be found at the bottom of the page.

Core Courses – 5 Courses

By the end of the core, students should be familiar with problems, issues, and applications in Biomedical Informatics, and are expected to apply general theories and methods to solve problems.

BINF G4000 Acculturation to Programming and Statistics (Prof. Karthik Natarajan, fall) This course is targeted for biomedical scientists looking for working knowledge of programming and statistics. This is a fast-paced, hands-on course covering the following topics: programming basics in Python, probabilities, elements of linear algebra, elements of calculus, and elements of data analytics. Students are expected to learn lecture material outside of the classroom and focus on labs during class. All labs evolve around real-world biomedical and health datasets. Only open to DBMI enrolled students in our MA or PhD program. BINF G4000 must be taken fall term of entry. Instructor provides placement exam on first day of class. Students may test out of the course based on placement exam results.

BINF G4001 Introduction to Computer Applications in Health Care & Biomedicine (Prof. Nicholas Tatonetti, fallTaught on main (Morningside) campus.  An overview of the field of biomedical informatics, combining perspectives from medicine, computer science and social science. Use of computers and information in health care and the biomedical sciences, covering specific applications and general methods, current issues, capabilities and limitations of biomedical informatics. Biomedical Informatics studies the organization of medical information, the effective management of information using computer technology, and the impact of such technology on medical research, education, and patient care. The field explores techniques for assessing current information practices, determining the information needs of health care providers and patients, developing interventions using computer technology, and evaluating the impact of those interventions. BINF G4001 must be taken fall term of entry.

BINF G4003: Methods I: Symbolic Methods (Prof. Chunhua Weng, fall) Survey of foundational symbolic methods for modeling health information systems and for making those models explicit and sharable.  The topics cover clinical terminologies (e.g., ICD-9, SNOMED-CT, MeSH, UMLS), biomedical ontologies (e.g., GO, Disease Ontology, PharmGKB), knowledge representation, computerized practice guidelines, semantic interoperability, and text processing. Prerequisites: Acculturation to Programming and Statistics (BINF G4000) or permission of instructor.

BINF G4002: Methods II: Computational Methods (Prof. Adler Perotte, spring) Survey of the computational methods underlying the field of medical informatics. Explores techniques in mathematics, logic, decision science, computer science, engineering, cognitive science, management science and epidemiology, and demonstrates the application to health care and biomedicine.

BINF G6002 Methods III: Research Methods (Prof. Lena Mamykina, spring) for Clinical, Public Health or Translational students. Provides an overview of research methods relevant to biomedical informatics. The overall goal of the course is to prepare the student to participate in and perform scientific research. Competencies of the course include learning to design a study of a biomedical informatics resource; perform quantitative and qualitative analysis relating to a biomedical informatics resource; and write a biomedical informatics-related research proposal. By the end of the course, all trainees must be able to write a biomedical informatics-related research summary and complete certification in responsible conduct of research.

BINF G4013 Biological Sequence Analysis (Prof. Richard Friedman, spring) for Bioinformatics students. Taken in lieu of BINF G6002 Research Methods (Mamykina). Biological Sequence Analysis introduces the basics of sequential, structural, and functional genomics.  The course is both a lecture and lab course, in which students learn the basic bioinformatic principles and apply these principles through laboratory exercises. The course accommodates both students with a computational background with little previous biology, and students from a primarily biological background, with little previous computation. Topics include basic Unix, biological databases, sequence comparison, database searching, multiple sequence alignment, biological regular expressions, profile methods (including hidden Markov models), protein and RNA structure prediction,  mapping, primer design, genomic analysis, molecular phylogetics, and functional genomics including microarray analysis and pathway analysis. 

Objectives

Information Technology (IT)
Apply computer science and statistical techniques to manage data, develop software, and solve problems.
BIST P8105 Data Science
EECS E6893 Information Processing: Big Data Analytics
QMSS G4063 Data Visualization
COMS W4111 Introduction to Databases
COMS W4181 Security I
CSOR W4246 Algorithms for Data Science
COMS W4156 Advanced Software Engineering
CSOR W4231 Analysis of Algorithms
COMS W4444 Programming and Problem Solving
COMS E6111 Advanced Database Systems
COMS E6998 Cloud & Big Data

Quantitative (Quant)
Apply mathematical and computational techniques to analyze data and test hypotheses.
BINF G5001 Data Science for Mobile Health
Apply mathematical and computational techniques to analyze data and test hypotheses.
BMEB W4020 Comp Neuro: Circuits In Brain
HBSS 4199 or HBSS 4160 Introduction to Biostatistics (Teachers College – http://www.tc.columbia.edu/academics/resources/courses/)
QMSS G4063 Data Visualization
QMSS GR5058 Data Mining For Social Science
QMSS GR5067 Natural Language Processing for Social Sciences
COMS W4705 Natural Language Processing
COMS W4771 Machine Learning
COMS W4772 Advanced Machine Learning or COMS E6898 Topics: Information Processing: From Data to Solutions
EECS E6720 Bayesian Models for Machine Learning
IEOR E4540 Data Mining
ELEN E4903 Machine Learning
STAT W4026 Applied Data Mining
STAT W4107 or STAT GU4204 Statistical Inference
STAT W4240 Data Mining
COMS W4761 Computational Genomics
COMS W4995 Applied Machine Learning
COMS W4995 Causal Inference for Data Science
STAT G6104 Applied Statistics
STAT G6509/GR6701 Foundations of Graphical Models
BIST P6104/P6114 Introduction to Biostatistical Methods
BIST P8110 Applied Regression II
BIST P8116 Design of Medical Experiments
BIST P8157 Longitudinal Data Analysis
BIST P9120 Topics in Statistical Learning and Data Mining
ELEN E6690 Statistical Learning for Biological and Information Systems
EECS E6893 Big Data Analytics
IEOR 4720 Deep Learning
IEOR 4742 Deep Learning for OR and FE
COMS 6998-7 Statistical Methods for NLP

Qualitative (Qual)
Apply techniques that aid in the understanding of behavioral and social phenomena associated with health-related problems and with delivery of healthcare.
BINF G4008 001 Special Topics in Biomedical Informatics: Intelligent Decision Support: History, Paradigms, Applications

BINF G4008 002 Special Topics in Biomedical Informatics: Ethics and Fairness in Digital Health
BINF G6002 Research Methods (core for clinical and translational, but can count as Qualitative objective for data science emphasis and non-postdoc MAs when not taken for core requirement)
NURS N9352 Qualitative Research Design & Methods
COMS W4170 User Interface Design
ORL 6500 Qualitative research methods in organizations: Design and data collection
ORL 6501 Qualitative research methods in organizations: Data analysis and reporting.
ORL 6518 Methods of case study and analysis.
ORLJ 4009 Understanding behavioral research
ORLJ 5018 Using survey research in organizational consulting
B9506-001 (PhD) Organizational behavior

Domains

Students should be able to apply general methods and theories of informatics to one or more areas of specialization: data science, clinical informatics, translational informatics, bioinformatics, and public health informatics. MDs and Nurses are exempt from the clinical domain requirement and will pick substitute courses with their academic and/or research advisor.

Clinical:
BINF G4011 Acculturation to Medicine and Clinical Informatics
BINF G5000 Defining, Evaluating and Improving Quality in Health Care
PATH G6003 Mechanisms in Human Disease

Translational:
BINF G4006 Translational Bioinformatics
PATH G6003 Mechanisms in Human Disease
PHAR G8001 Principles of System Pharmacology
BIOT W4200 Biopharmaceutical Development & Regulation
COMS E6998 Computational Methods/High Throughput Sequencing

Bioinformatics:
BINF G4013 Biological Sequence Analysis
BINF G4017 Deep Sequencing
COMS W4761 Computational Genomics
BIOL W4510 Genomics of Gene Regulation
BIST P8119 Advanced Stat/Comp Methods Genetics/Genomics
ELEN E6010 Design Principles for Biological Circuits
BIOL W4799 Molecular Biology of Cancer

Public Health:
EPID P6400/02 Epidemiology
EPID P8471 Social Epidemiology
SOSC P8795 New Media and Health
BIST P6530 Issues & Approaches in Health Policy & Management
EHSC P6385/6 Principles of Genetics and the Environment I and II

Research

Students should conduct independent research in Biomedical Informatics; including the ability to formulate a hypothesis, design a suitable experiment, and carry it out with sensitivity to ethical standards.

BINF G6001 Projects in Biomedical Informatics. Taken at least once for MA students and every fall and spring term for PhD students until successful passage of Oral II/Depth Exam. MA students enroll for 3 points. First year PhD students enroll for 6 points fall and spring terms; 9 points fall and spring terms in 2nd year; 12 points fall and spring terms each subsequent year until enrollment in BINF G9001. NLM funded postdoctoral MA students enroll for 6 points fall and spring terms of first year, and 9-12 points for fall and spring terms of their 2nd and 3rd years, depending upon their course load.
BINF G9001 Doctoral Research in Biomedical Informatics. Taken the term following successful passage of the 2nd preliminary PhD exam, the Oral II/Depth Exam.
BINF G9999 Doctoral Dissertation. Taken in the final term of enrollment for PhD students along with BINF G9001.
CMBS G4010 Responsible Conduct of Research and Related Policy Issues
BINF G8010 Teaching Experience; Teaching can prepare educational materials, deliver lectures, and evaluate students.
BINF G4099 Research Seminar;  ColloquiaIs familiar with investigators, institutions, projects, methods and theories in the field locally and at other institutions.
BINF G8001 Independent Readings

Other Requirements

Ethics Course
All doctoral and postdoctoral students are required to take the Ethics Course (CMBS G4010 Responsible Conduct of Research and Related Policy Issues, 1 pt) during the Spring semester of their first year in the program. The ethics course satisfies a National Institutes of Health requirement.

MPhil Course 
BINF G8010 MPhil Teaching Experience, 2 pts
Serving as a Teaching Assistant (TA) is a GSAS degree requirement for PhD students and a DBMI degree requirement for postdoctoral students. PhD students TA for 2 courses. (BINF G8010 MPhil Teaching Experience, 2 pts) MD-PhD students TA for 1 course. Postdoctoral MA students TA for one (two year postdoctoral students) or two courses (three year postdoctoral students). Students are solicited for TA preferences over email Spring term. Final assignments are made by the Graduate Program Director.

Research Seminar
BINF G4099 Research Seminar, 1 pt, P/F
Enrollment in the Research Seminar is required for PhD students. Bioinformatics students may attend the C2B2 seminar in their second year and each subsequent year in lieu of the Research Seminar. Full-time MA students are expected to enroll in the Research Seminar. Part-time MA students are not required to enroll if doing so would cause them to enter the next residence unit category. However, they are expected to attend whenever feasible. Passing the Research Seminar is dependent upon attendance.

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