SyllabusOverview: As described in the course overview, Cancer Bioinformatics will focus on modern computational approaches for analyzing high-throughput "omic" data in cancer. Many papers we will discuss were published in the past year or two. Accordingly, there is no textbook for the course. However, the book Cancer Bioinformatics is freely available electronically through the University of Wisconsin library subscription and covers complementary material including background information on cancer biology, descriptions of the most prevalent types of "omic" data, and numerous case studies. A recent review summarizes the major stages in a cancer bioinformatics pipeline, including variant calling and other topics that are out of the scope of this course. The Emperor of All Maladies is the Pulitzer Prize-winning "biography" of cancer that was mentioned in class.
Objective: The primary goal of the course is to introduce cancer biology, state-of-the-art algorithms, data resources, and open problems to enable students to pursue their own research in cancer bioinformatics.
Prerequisites: The official prerequisite is Introduction to Bioinformatics (BMI/CS 576). Ideally, students would be familiar with basic concepts from cell biology/genetics (e.g. genes, cell cycle, etc.) and computer science/machine learning (e.g. graph algorithms, regression, clustering, etc.). No student will have the perfect background, therefore it is critical that students ask for clarification in class when they are unfamiliar with biological or computational concepts. In some cases, the instructor will recommend additional reading material to help explain these concepts in greater depth.
Office hours: Because the instructor's office is in the Discovery Building, which has restricted access, office hours will be by appointment only. Generally there will be blocks of time reserved for office hours meetings on Tuesday and Thursday mornings.
Grades: There will be no homework or exams. Students will be graded on:
Presentations: Students will be scheduled to present research papers from the reading list on a rotating basis and should expect to present about 4 papers in the semester. The student assigned to lead the paper discussion will guide the class through the main methods and results. The leader will close the discussion with ideas about how to improve the study or other potential future work. The leader may optionally prepare slides with figures and equations from the paper as visual aids. All other students should read the paper before class and bring a copy with them (printed or electronic).
Project: Students will complete projects in groups of 2 or 3 and must refrain from discussing their project with anyone outside of their group unless prior approval is granted by the instructor. Projects will be in the style of a computational biology research paper; for examples see the RECOMB 2014 and ISMB 2014 proceedings. Strong projects have the potential to be submitted to a similar conference or journal, and the instructor will continue to support student projects after the semester in such cases. Students are encouraged to use and build upon existing data repositories and tools but must thoroughly reference all external resources. Any standard programming language is permitted as long as the project submission includes an executable or scripts to reproduce all computational analysis. Plagiarism - including using text, images, or code without attribution - will not be tolerated and will be dealt with in accordance with the Academic Misconduct Process. The project page presents project ideas and additional guidelines.