Below are areas of study that bioinformatics students should be proficient in. For more details on the Bioinformatics courses please see the course listing page.
Please note that both BIOINF 580 and BIOINF 585 can count towards either advanced bioinformatics program requirements or computing requirements. A maximum of 1 can count towards computing requirements, however both may count towards meeting advanced bioinformatics requirements.
For example curriculum tracks to follow, see our Bioinformatics Tracks:
2021-2022-PDF | 2022-2023-PDF | 2023-2024-PDF
If any questions about these or other courses please meet with a faculty adviser for assistance.
Introductory Bioinformatics
Bioinformatics PhD students must take BIOINF-529. Master’s students may take either one of the following:
- BIOINF-527: Introduction to Bioinformatics & Computational Biology
- BIOINF-529: Bioinformatics Concepts and Algorithms
Computing and Informatics
- BIOINF-575: Programming Laboratory in Bioinformatics
- BIOINF 576: Tool Development for Bioinformatics
- BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences
- BIOINF-585: Deep Learning in Bioinformatics
- BIOSTAT-615: Statistical Computing
- BIOSTAT-625: Computing with Big Data
- EECS-402: Computer Programming For Scientists & Engineers
- EECS-453: Applied matrix algorithms for signal processing, data analysis and machine learning
- EECS-545: Machine Learning
- EECS-551: Matrix Methods for Signal Processing, Data Analysis and Machine Learning
- EECS-553 : Machine Learning (ECE)
- EECS-587: Parallel Computing
- EECS-592: Foundations of Artificial Intelligence
- HS-650: Data Science and Predictive Analytics
- LHS-610: Learning from Health Data: Applied Data Science in Health
- STATS-507: Modern Data Analysis
Probability & Statistics
Master’s students may take just BIOSTAT 521 or PSYCH 613 to satisfy program requirements. Ph.D. students must take the sequential pair of BIOSTAT 521 + 522 or PSYCH 613 + 614 to satisfy program requirements. Other approved pairs include STATS 425 + 426 and BIOSTATS 601 + 602. If a student takes only 1 of the 2 courses, that is insufficient. In addition, the student must receive a passing grade (“B” or better) in at least the 2nd course.
- BIOSTAT-521: Applied Biostatistics
- BIOSTAT-601: Probability & Distribution Theory
- BIOSTAT-602: Biostat Inference
- MATH-526: Discrete State Stochastic Processes
- PSYCH-613: Advanced Statistical Methods
- PSYCH-614: Advanced Statistical Methods
- MATH/STATS-425: Introduction to Probability
- STATS-426: Introduction to Theoretical Statistics
- STATS-500: Applied Stat I
- STATS-511: Statistical Inference
Molecular Biology
- BIOINF-523: Introductory Biology for Computational Scientists
- BIOLCHEM-515: Intro Biochem
- BIOLCHEM-650: Eukaryotic Gene Transcription (*Note: This course is only 2 cr. hrs. It is only approved to satisfy the biology requirement if taken in conjunction with one other course; please speak with an adviser for details.)
- BIOLCHEM-660: Molecules of life: Protein structure, function and dynamics
- CDB-530: Cell Biology
- CDB-550: Histology
- CBD-581: Developmental Genetics
- HUMGEN-541: Molecular Genetics (No longer offered, accepted if taken in the past.)
- HUMGEN-542: Molecular Basis of Human Genetic Disease (No longer offered, accepted if taken in the past.)
- HUMGEN-545: Molecular, Cellular, and Organismal Genetics (Only for students who have had undergraduate genetics or taken BIOINF-523)
- HUMGEN-546: Genetics in Human Biology and DiseaseMCDB-427: Molecular Biology
- MCDB-428: Cell Biology
- NEUROSCI-601: Principles Neuroscience II
- PHRMACOL-501: Chemical Biology
- PHRMACOL-601: Principles of Pharmacology
- PHYSIOL-502: Human Physiology
Advanced Bioinformatics & Computational Biology
Two courses, among them at least one BIOINF
- BIOINF-463: Mathematical Modeling in Biology
- BIOINF-520: Computational Systems Biology in Physiology
- BIOINF-528: Structural Bioinformatics
- BIOINF-540: Mathematics of Biological Networks
- BIOINF-545: High-throughput Molecular Genomic and Epigenomic Data Analysis
- BIOINF-547: Mathematics of Data (Formerly Probabilistic Modeling in Bioinformatics)
- BIOINF-551: Proteome and Metabolome Informatics
- BIOINF-563: Advanced Mathematical Methods for Biological Sciences
- BIOINF-568: Mathematics and Computational Neuroscience
- BIOINF 576: Tool Development for Bioinformatics
- BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences
- BIOINF-585: Deep Learning in Bioinformatics
- BIOINF-590: Image Processing and Advanced Machine Learning for Cancer Bioinformatics
- BIOINF-593: Machine Learning in Computational Biology
- BIOINF-597: Artificial Intelligence for Medicine and Biomedical Sciences
- BIOINF-665/BIOSTAT-665/HUMGEN-665: Statist Popul Genetics
- BIOSTAT-666: Statistical Methods in Human Genetics
- BIOSTAT-830: Advanced Topics in Biostatistics
- BME/EECS-516: Medical Imaging Systems
- CMPLXSYS-510/MATH-550: Adaptive Dynamics: The mathematics of sustainability
- CMPLXSYS-530: Computer Modeling (will only count when the topic is relevant to bioinformatics)
- EHS-674: Environmental and Health Risk Modeling
- LHS-712: Natural Language Processing for Health
- STAT-710: Special Topics in Theoretical Statistics I
Electives
Most graduate level courses in BIOINF, BIOLOGY, BIOSTATS, EECS, LHS, or STATS can be taken as elective.
Seminars / Discussions
- BIOINF-602: Journal Club (This course is for first-year students who have not taken a journal club before.)
- BIOINF-603: Journal Club
Bioinformatics Courses for Non-majors
- BIOINF-524: Foundations for Bioinformatics
- BIOSTAT-607: Basic Computing For Data Analytics
Basic Skills and Rigor
- BIOINF-500: Skills to Succeed in the Bioinformatics Graduate Program and Beyond
- PIBS-503: Research Responsibility & Ethics
- BIOINF-504: Rigor and Transparency to Enhance Reproducibility