Relevant Courses

Ph.D. students must take some of the required courses in pairs in order to satisfy the statistics requirement (e.g., STATS 425 & 426 or BIOSTATS 601 & 602). If a student takes only 1 of the 2 courses, that does not satisfy the program requirement; the student must take both, and receive a passing grade in at least the 2nd course.

Introductory Bioinformatics

BIOINF-527 Introduction to Bioinformatics & Computational Biology

Computing and Informatics

BIOINF-575 Prog Lab in Bioinf
BIOINF-580 Biomedical Signal and Image Analysis
BIOINF-585 Machine Learning for Systems Biology and Clinical Informatics (can also count for advanced BIOINF)
BIOSTAT-615 Statistical Computing
EECS-402 Computer Programming For Scientists & Engineers
EECS-545 Machine Learning
EECS-587 Parallel Computing

Probability & Statistics  

BIOSTAT-521 Applied Biostatistics (MS only)
BIOSTAT-601 Prob&Distrib Theory
BIOSTAT-602 Biostat Inference
MATH-526 Discrete State Stochastic Processes
STATS-412 Introduction to Probability and Statistics (MS only)
MATH/STATS-425 Introduction to Probability
STATS-426 Introduction to Theoretical Statistics
STATS-500 Applied Stat I

Molecular Biology  

BIOINF-523* Bioinformatics Basic Biology Lab (intro – insufficient alone)
BIOLCHEM-452 Advanced Biochemistry II
BIOLCHEM-515 Intro Biochem
BIOLCHEM-550 Macromol Struc&Func
BIOPHYS-608 / MCDB-608/ PHYSICS-608 Biophysical Principles of Microscopy
CDB-530 Cell Biology
HUMGEN-541 Gene Structur&Regul
HUMGEN-542 Molec Basis HG Dis
MCDB-427 Molecular Biology
MCDB-428 Cell Biology
NEUROSCI-601 Princ Neurosc II
PHARM-501 Introduction to Pharmacology
PHYSIOL-502 Human Physiology
PHYSIOL-555 Integrative Genomics

Advanced Bioinformatics & Computational Biology

Two courses, among them at least one BIOINF

BIOINF-463 Mathematical Modeling in Biology
BIOINF-520/ PHYSIOL-520 Computational Systems Biology in Physiology
BIOINF-528 Advanced Applications of Bioinformatics
BIOINF-545 High-throughput Molecular Genomic and Epigenomic Data Analysis
BIOINF-547 Probabilistic Modeling in Bioinformatics
BIOINF-551 Proteome Inf
BIOINF-563 Advanced Mathematical Methods for Biological Sciences
BIOINF-585 Machine Learning for Systems Biology and Clinical Informatics (can also count for computing)
BIOINF-665/ BIOSTAT-665/ HUMGEN-665 Statist Popul Genet
BIOINF-800 Special Topics: Mathematics of Biological Networks
BIOINF-800 / MATH-559 Computational and Mathematical Neuroscience
BIOSTAT-666 Num Meth Hum Gen
BIOSTAT-830 Advanced Topics in Biostatistics
CMPLXSYS-510 / MATH-550 Adaptive Dynamics: The mathematics of sustainability
CMPLXSYS-530 Computer Modeling
EECS-598 Data Science for Medicine (not other topics of this course)
STAT-710 Special Topics in Theoretical Statistics I


Any of the above courses if taken more than required. Most graduate level courses in biostats, stats, EECS or biology can be taken as elective. BIOINF 525 is not acceptable as elective.

Seminars / Discussions

BIOINF-602 Journal Club

Bioinformatics Courses for Non-majors

BIOINF-525 Foundations in Bioinformatics & Systems Biology
BIOINF-606 Bioinformatics courses for non-majors