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Computational Biology focuses on algorithms for analyzing biological data. The course will introduce the main topics in this area, such as analysis of genome sequences, protein structures, gene networks, and so on. We will cover the fundamental algorithms for these tasks, and particularly examine the role of machine learning and data mining in computational biology.

Learning Objectives

After taking this course students will be

  • knowledgeable about the fundamental computational biology tasks like sequence and structure analysis and evolution, biological networks, and machine learning methods in bioinformatics
  • able to understand the key algorithms for the main tasks
  • able to implement and apply the techniques to real world datasets

The pre-requisites for this course include intro to algorithms, and some probability & statistics. Knowledge of basic linear algebra will serve you well too. Assignments will require the use of Python.


There is no required text for the course. Reading materials will be posted online.

Grading Policy

Your grade will be a combination of the following items.

  • Assignments (37%): The assignments are practically oriented; you'll be asked to implement algorithms and apply them to real datasets, to complement the theory. The will be 6-7 assignments during the semester.
  • Exams (40%): There will be two exams covering the main topics of the course. The tentative exam dates are posted on the class schedule table. There is no comprehensive final exam. All exams are open book.
  • Project (20%): You will be required to choose a project exploring a topic. You will read papers on that topic, implement and compare state-of-the-art methods, and write a report on your findings. Finally, you will present your project in class.
  • Attendance (3%): Students are required to attend and participate in the class.

Late Assignments: Most assignments will be due just before midnight on the due date. Students can get an automatic one day extension for a 15% grade penalty. No late assignments will be accepted after the midnight following the due date.

Academic Integrity

Students must work independently on all course assignments. You may consult other members of the class on the assignments, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source. Copying and pasting from published sources or the internet is considered plagiarism and is not acceptable. Plagiarized work will receive an automatic grade of zero.

Student-teacher relationships are built on trust. Acts which violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities and The Rensselaer Graduate Student Supplement define various forms of Academic Dishonesty and procedures for responding to them. Submission of any assignment that is in violation with these policies will result in a penalty that is deemed by the instructor to be appropriate to the infraction ranging from a grade of zero on the assignment in question, to failure of the class as a whole. The student will also be reported to the Dean of Students or the Dean of Graduate Education as appropriate. Note that academic dishonesty will be dealt with severely and will be reported to the Dean of Students. If you have any questions concerning this policy before submitting an assignment, please ask for clarification.

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Page last modified on January 13, 2019, at 06:27 PM