It has been known for decades that cancer is driven largely by mutations that accumulate in an s genome during their lifetime. However, it was only in 2008 that the first cancer genome was sequenced, an advance made possible by new DNA sequencing technologies. Cancer sequencing data is now being generated at an exponentially increasing rate. The challenge has shifted from producing cancer genome sequencing data to interpreting this data in order to advance cancer biology and treatment. This seminar will explore algorithms, statistical methods, and techniques from machine learning that address four important challenges in cancer genome sequencing and interpretation.
No biology background is assumed. Necessary background will be
introduced in lectures and reading. Computer Assignment. Proposal:Due TBD (Specific Aims and Significance) and TBD (All). Previous offerings of this course are available here:
The course will be organized in seminar style where students will read
and present recent papers on the topics listed below. Each topic
will be introduced with background lectures. Students will
undertake a project to further study one of the topics. To the
extent possible, projects will be adjusted to the
background/interest of the student and could range from theoretical
(e.g. designing a new algorithm and proving its correctness), to
the practical (a software implementation).
Paper reviews are due BEFORE the class when the paper is
presented. Email your review to me using this review form.
Course Credits (for Computer Science students)
No biology background is assumed. Necessary background will be introduced in lectures and reading.
Proposal:Due TBD (Specific Aims and Significance) and TBD (All).
Previous offerings of this course are available here: