Courses Curriculum

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: 2017-2018 - PDF | 2018-2019 - PDF.

If any questions about these or other courses please meet with a faculty adviser for assistance.

Introductory Bioinformatics

  • BIOINF-527: Introduction to Bioinformatics & Computational Biology
  • BIOINF-529: Introductory Bioinformatics

Computing and Informatics

  • BIOINF-575: Programming Laboratory in Bioinformatics
  • BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences
  • BIOINF-585: Deep Learning in Bioinformatics
  • BIOSTAT-615: Statistical Computing
  • EECS-402: Computer Programming For Scientists & Engineers
  • EECS-445: Introduction to Machine Learning
  • EECS-545: Machine Learning
  • EECS-587: Parallel Computing
  • LHS-610: Learning from Health Data: Applied Data Science in Health

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 Ph.D. 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
  • STATS-412: Introduction to Probability and Statistics 
  • 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*: Bioinformatics Basic Biology Lab (intro – insufficient alone)
  • BIOLCHEM-452: Advanced Biochemistry II
  • BIOLCHEM-515: Intro Biochem
  • BIOLCHEM-550: Macromol Structure & Function
  • 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.) 
  • CDB-530: Cell Biology
  • CDB-595: Biology of Regeneration
  • HUMGEN-541: Molecular Genetics
  • HUMGEN-542: Molecular Basis of Human Genetic Disease
  • MCDB-427: Molecular Biology
  • MCDB-428: Cell Biology
  • NEUROSCI-601: Principles Neuroscience II
  • PHARM-501: Introduction to Pharmacology
  • PHARM-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: Probabilistic Modeling in Bioinformatics
  • BIOINF-551: Proteome Informatics
  • BIOINF-563: Advanced Mathematical Methods for Biological Sciences
  • BIOINF-568: Mathematics and Computational Neuroscience
  • BIOINF-580: Introduction to Signal Processing and Machine Learning in Biomedical Sciences 
  • BIOINF-585: Deep Learning in Bioinformatics
  • BIOINF-665/BIOSTAT-665/HUMGEN-665: Statist Popul Genetics
  • BIOSTAT-666: Statistical Methods in Human Genetics
  • BIOSTAT-830: Advanced Topics in Biostatistics
  • 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 
  • 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. BIOINF-525 is not acceptable as elective.

Seminars / Discussions

  • BIOINF-601: Bioinformatics Seminar
  • 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
  • BIOINF-525: Foundations in Bioinformatics & Systems Biology
  • BIOINF-527: Introduction to Bioinformatics & Computational Biology
  • BIOINF-606: Introduction to Bio-computing