Introduction to Computational Biomedicine and Health (BINFGU4001 & COMS W4560)
Instructor: Prof. Gamze Gürsoy (gg2845 at cumc.columbia.edu)
TAs: Florent Pollet (ffp2106 at cumc.columbia.edu), Zoe Ji (cj2837 and cumc.columbia.edu), and Jacob Blindenbach (jb4816 at columbia.edu)
Time/date: Tuesday/Thursday 4:10pm-5:25pm Location: MLK 606 (Morningside campus)
Course schedule and the topics of the class can be found here.
Course Description
This course offers a comprehensive introduction to the core computational methods driving modern biomedical research and health data science. As biological and clinical datasets grow in scale and complexity—from genomic sequences and molecular profiles to electronic health records (EHRs) and consumer health data—this course equips students with the essential computational foundations to model, analyze, and interpret high-dimensional biomedical data.
Organized around key algorithmic challenges spanning Clinical Informatics, Consumer Health Informatics, and Bioinformatics, the course focuses on the design and application of algorithms and statistical models to solve real-world biomedical problems. Lectures emphasize practical techniques and showcase their use across diverse biomedical data types.
Designed for advanced undergraduates and graduate students in biomedical informatics, computer science, biomedical engineering, applied mathematics, and related fields, this course builds a rigorous understanding of computational biomedicine. It serves as a core requirement for the Biomedical Informatics PhD and master’s programs and is cross-listed with Computer Science. Students interested in careers in bioinformatics, health data science, computational biology, or biomedical AI will find this course especially valuable.
Prerequisites: There are no formal prerequisites. While prior experience with informatics is helpful, it is not required.
As biomedical data continue to expand in size, diversity, and impact, this course provides a critical foundation in the algorithmic, statistical, and computational tools needed to advance the future of research at the intersection of computation and human health.
Goals for student learning
By the end of Introduction to Computational Biomedicine and Health (BINF4001 & COMS W4560), students will be able to:
- Understand the core computational methods that underpin biomedical research and health data science, including algorithm design, statistical modeling, and data integration techniques applied to biological and clinical data.
- Identify and analyze key challenges in Clinical Informatics, Consumer Health Informatics, and Bioinformatics, and learn how to apply appropriate computational approaches to address real-world problems in each domain.
- Develop proficiency in understanding computational studies using high dimensional data such as genomic sequences, molecular phenotypes, electronic health records (EHRs), and wearable sensor data, with an emphasis on application of AI.
- Critically evaluate biomedical studies and data-driven health applications, including understanding issues of bias, generalizability, reproducibility, and ethical considerations in biomedical data science.
These learning goals are designed to give students a rigorous foundation in computational biomedicine while fostering problem-solving, critical thinking, and communication skills essential for research and careers in health data science, bioinformatics, and biomedical AI.
Course policies:
Attendance: Following each lecture, students are required to submit a concise, half-page summary of the material covered. Summaries are due at the start of the next class session. Each homework is worth 1 point, and students are allowed to miss up to 3 submissions without penalty.
Academic Integrity: Generative AI tools (such as ChatGPT or similar platforms) are strictly prohibited for any submissions. Violations will be treated as breaches of Columbia’s academic integrity policy. Please review Columbia’s full academic integrity guidelines:
Violations will result in disciplinary action in accordance with university policy.
Academic Accommodations: Students requiring accommodations must register with the Office of Disability Services (ODS). All accommodations will be handled confidentially.
Course-Specific Guidelines:
- Devices: Laptops and tablets are permitted for note-taking and course-related activities. Please avoid unrelated use to maintain a focused learning environment.
- Communication: Announcements, materials, and deadlines will be posted on CourseWorks. Regularly check for updates and use the platform for submitting assignments and asking questions.
- Attendance: This class is in-person only; remote attendance is not offered or permitted.
Required Texts and Materials:
There are no required textbooks for Introduction to Computational Biomedicine and Health (BINF4001 & COMS W4560). All required readings supplemental materials will be provided through CourseWorks or linked from the course website ahead of the lectures and all lecture slides will be provided through CourseWorks or linked from the course website after the lectures.
Access and Expectations: All required materials will be available in electronic format via CourseWorks; no hard copies are necessary. There are no required editions or versions of any texts. If supplemental readings are recommended, details will be provided, but students may choose the format (electronic or print) that works best for them. Students are responsible for ensuring they have timely access to all posted materials. Complete information on required course materials (including any readings, software, or other resources) will be posted to CourseWorks prior to the start of early registration for the term.
Course Requirements and Grading Breakdown:
Students will be evaluated based on multiple assignments designed to assess their understanding and application of core concepts in computational biomedicine. Grading is based solely on academic performance, with clear criteria provided for each component.
Grading Breakdown:
| Component | Points | Percentage of Final Grade | Details |
| Lecture Summaries (Homework) | 19 points | 19% | Half-page summaries of each lecture. Up to 3 may be missed without penalty. |
| Term Paper | 33 points | 33% | Due December 2, 2025, at 5 PM EST. |
| Final Project | 44 points | 44% | Includes design, anticipated execution, and presentation of an original project. |
| Final Presentation Attendance | 4 points | 4% | Mandatory attendance at final presentations. |
| Total | 100 points | 100% |
Rubrics and Evaluation Criteria
Lecture Summaries (Homework) – 19% of Final Grade
Students must submit a concise (half-page – 12pt font, single spaced) summary following each lecture, focusing on core concepts and key takeaways. They are due at the beginning of the next lecture.
| Criteria | Excellent (1 point) | Incomplete (0 points) |
| Content Understanding | Clearly and accurately captures key topics, concepts, and discussions from the lecture. | Summary is missing, incomplete, or does not reflect key content. |
| Clarity and Writing | Well-organized, coherent, and clearly written. | Poorly organized or difficult to follow. |
| Originality | Written in the student’s own words without copying slides or external materials. | Largely copied or generated by unauthorized tools (including AI). |
Up to 3 summaries may be missed without penalty. Submitting more than the required number will earn you bonus points.
Term Paper – 33% of Final Grade
A written research paper exploring a topic in biomedical informatics. The paper should demonstrate deep engagement with course concepts and independent research.
This paper enables students to delve more deeply into a topic of personal interest in the context of biomedical informatics. Generally, this paper should be either a review of a particular issue or technology in biomedical informatics, or a well-researched position paper expressing a personal view on a controversial topic in the field.
The paper must have labeled sections for Background, Current Approaches, Discussion (Limitations and Future Directions), and Conclusion. Grading will be based primarily on the writer’s ability to discuss a compelling argument about the chosen topic, with at least three concrete examples, identified gaps in research, and included citations. (Note that only published scientific papers are acceptable to cite) The discussion section focuses on limitations from the current approaches and possible directions for future work for the selected topic within the scope of feasibility. The paper must include your name and a title, and be a maximum of 2500 words, double spaced, 12 point Times New Roman font, not including figures and references. Any citation style is acceptable, as long as it is consistent throughout the essay. Spelling, grammar, and overall readability will be considered in the grading as well.
| Criteria | Excellent (A-level) | Good (B-level) | Needs Improvement (C or below) |
| Topic and Scope | Focused, original, and clearly relevant to biomedical informatics. | Relevant topic but somewhat broad or underdeveloped. | Topic lacks relevance or clarity. |
| Research and Evidence | Strong use of scholarly sources, well-integrated evidence. | Adequate sources with some gaps or weak integration. | Insufficient or inappropriate sources. |
| Insight | Demonstrates critical thinking and original insights. | Some insight but primarily descriptive. | Little to no insight; mostly summary. |
| Organization and Clarity | Logical structure, clear argument, and smooth flow. | Some structural issues or unclear sections. | Disorganized and difficult to follow. |
| Writing Quality | Grammatically correct, polished, and professional. | Minor grammatical errors. | Frequent grammatical issues. |
| Citation and Ethics | Properly cited throughout; no plagiarism. | Minor citation errors. | Significant citation problems or plagiarism. |
Due: 5 PM EST, December 2nd, 2025
Final Project Presentation – 44% of Final Grade
The instruction team will assign students into groups of 3–4, which will be announced on October 9. Students will work collaboratively within their group to design and present a computational solution addressing a biomedical problem of their choice. Each project must explicitly incorporate at least two modules from the course (clinical informatics, consumer health informatics, bioinformatics). The presentation will take place during the last week of class. All groups must submit their presentation slides (PPT format) to Courseworks by November 25, 2025, 4:00 PM. No exceptions.
Presentation Evaluation Criteria (44 points total):
| Criterion | Excellent (11 pts) | Good (8-9 pts) | Needs Improvement (0-7 pts) |
| Content | Comprehensive and clearly covers all required sections with strong depth and accuracy. | Covers required sections but with limited depth or minor inaccuracies. | Sections incomplete, superficial, or unclear. |
| Delivery | Clear, confident, engaging, and professional. | Adequate delivery, some hesitation or minor engagement issues. | Delivery unclear, disengaged, or lacking professionalism. |
| Visual Aids | High-quality visuals that significantly enhance understanding and clarity. | Adequate visuals, minor improvements needed. | Visuals poor quality, unclear, or absent. |
| Timing and Flow | Presentation well-paced, logically structured, within allotted time. | Slight timing issues, minor structural concerns. | Poor pacing, significantly over/under time, disorganized flow. |
Attendance at All Presentations: 4 points
Mandatory attendance during all final presentations.
Required Presentation Content and Project Description:
It is not by the matter of random chance that in this course you are being exposed to three different areas of biomedical informatics — Bioinformatics, Consumer Health Informatics, and Clinical Informatics. This was made by design: biomedical informatics in the real world is very interdisciplinary. To make a real impact in health, one has to think outside the box of their specialty, learn about methods and ideas of other research fields, and collaborate with other researchers that can bring their own viewpoint to the table. A great deal of major scientific advances stems from investigators learning how to do just that, sustainably and repeatedly. Throughout your careers, whatever aspect of science they may touch, you will acquire this invaluable skill. This project is your first step on this exciting journey.
Your task is to design a study that integrates at least two of the three modules from this course to solve a real-world health problem. By doing so, you will integrate different topics you have learned in the course into a collaborative research project. Final delivery will consist of a presentation where you pitch your research ideas, advocating for the significance of a problem, proposing a potential solution, the benefit of a multimodal or cross-disciplinary approach to that solution, and anticipated challenges. The problem should be specific: choose a disease or problem area that you would like to focus on, and how different topics from the course can help tackle a problem within that domain.
For some examples, think about how you would approach the following tasks:
- Integrate and analyze clinical and omics for a targeted prediction?
- Design a mobile health app to deliver patient recommendations based on a set of clinical observations?
- Build a clinical decision support tool integrating biomarker data?
Each section below should have at least one dedicated slide. Additional slides are encouraged for clarity and depth.
- Modules: Explicitly state the course modules integrated (clinical informatics, consumer health informatics, bioinformatics).
- Problem: Clearly define the biomedical problem or question. Specify if focusing on a disease, behavior, or another biomedical issue.
- Current Approaches: Describe existing methods addressing the problem and clearly explain your project’s novel contribution or improvement.
- Population: Clearly specify who benefits from your solution (e.g., researchers, clinicians, patients, general public).
- Data: Describe available data sources, their relevance, and how they can be integrated effectively.
- Methods: Clearly outline your computational solution, detailing the methodological approach and execution plan.
- Evaluation: Define clear, specific criteria for assessing your solution’s success, including potential outcomes and validation strategies.
- Delivery Method: Clarify your deliverable, the intended audience, and how it will be provided or implemented.
Feedback Timeline:
Project feedback: Provided after presentations (oral feedback).
Homework feedback: Within one week of submission.
Term paper feedback: Returned by the end of the semester.