CSE 391: Computational Biology

Fall, 2017
Mon & Fri: 1:00-pm-2:20pm.

Instructor: Martin Radfar
Email: radfar@cs.stonybrook.edu
Office hours: Tue and Thu 2pm-3:30pm, or by appointment. Office: CS 131
TAs:TBD

Announcements

Course Description

This course is an introduction to the field of computational biology and deals with the practical and hands-on approaches in the field. We introduce the fundamental biological problems concerning functional elements of genomes, disease, and cancer, as well as algorithmic and machine learning methods to tackle these biological problems. In addition, the course addresses the statistical techniques that are indispensible parts of evaluating the significance of discoveries or predictions for biological problems. At the beginning of each class, we introduce a real-world biological example pertaining to the lecture topic of the day and address the machine learning technique(s) to tackle the problem

Topics

  1. A brief introduction to molecular biology (e.g. central dogma)
  2. Comparing genome (e.g. HMM, dynamic programing, sequence alignment)
  3. Gene and genome regulation (e.g. clustering, PCA, gene expression analysis)
  4. Biological network analysis (e.g. Bayesian networks, pathway analysis)
  5. Cancer genomic (e.g. identifying cancer driver mutations)
  6. Statistical evaluation techniques (e.g. p-value, hypothesis testing, AUC)
  7. Introducing the most frequently applied machine learning approaches in genomic and proteomics data analysis (e.g. dimensionality reduction, (un) supervised learning, clustering)
  8. Preprocessing methods for genomic data (e.g. cleaning noisy data, normalization)

Assignments

  • TBD

    • Textbook

    There is no specific textbook that covers all topics. For each topic, we introduce the appropriate book(s) or reading materials. However, a rather comprehensive reference is: Kellis, Manolis, ed. Computational Biology: Genomes, Networks, Evolution. Available at: here.

    Grading Policy

    Academic Integrity Statement

    Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty are required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Technology & Management, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty, please refer to the academic judiciary website at here.

    Disability Support Services

    If you have a physical, psychological, medical or learning disability that may impact your course work, please contact Disability Support Services, 128 ECC Building (631) 632-6748. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential.

    Students who require assistance during emergency evacuation are encouraged to discuss their needs with their professors and Disability Support Services. For procedures and information go to the following web site: http://www.ehs.sunysb.edu and search Fire Safety and Evacuation and Disabilities.