Computational Biology
This document describes the course objectives and
organisation, and contains a course timetable. Why do you want to study Computational Biology? Here is an answer from Barack Obama .
The course will teach a variety of contemporary
approaches to the
theory and implementation of Computational Biology, with emphasis
on theory, software and algorithms. It equips students with sufficient
knowledge to enable employment, or postgraduate
study involving Comp Biol (Brain and AI related reserach), or in general Big Data related areas.
2.1 Tutors for the course
Jianfeng
Feng
Room 313 DCS email: jianfeng.feng@warwick.ac.uk
Ye YAO email: bioyyao@gmail.com
2.2 Teaching methods
Lectures: 3 per week, held at the following times
Day
Time
Place
Mon 15.00-17.00 CS1.01
Thur
15.00-16.00
CS1.01
These lectures will cover the introductory theory
behind the topics above, as well as various topics related to these (e.g.
implementation issues).
All students on this course will have a supervised
exercise class every week, starting from the first week. Details of times, and
which exercise class you should attend are given during lectures.
A lot of the work on the course is based on exercises
that you should do with or without the computer. In addition to the
timetabled exercise classes you are expected to spend many hours a
week both using the computer, and reading supporting material from the
reading list in order to deepen your understanding of the topics being
covered. All students experience problems learning new concepts and
skills and it is important not to be discouraged and give up. If you get stuck
ask other students, a demonstrator, or a tutor for help. As stated
already, various exercises may be set during the course.
Remember that the lectures for this (and any other)
course really aim to provide you with the minimal essential information on a
subject. To get a deeper understanding (and in order to do well in the exams,
and in subsequent courses which build on these) you MUST read around the
subject. The reading list at the end of this handout should provide some
good staring points.
Assessment for this course will be based:
Three-hour examination (60%) Coursework
(40%)
The list below gives a provisional week-by-week list
of topics (note these may change depending on how the course progresses).
Some lectures will incorporate important announcements, including changes in
later lectures, so if you ever miss a lecture make sure you find out from
another student exactly what was said.
Note: I will update the slides below each week after the lecture.
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Week
1
Introduction and MRI physics
[slides_2015 (pdf)]
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Week
2
Preprocessing
[slides_2015 (pdf)]
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Week
3
Time series analysis: models
[slides_2015 (pdf)]
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Week
4
Time series analysis: fitting
[slides_2015 (pdf)]
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Week
5
Time series analysis: networks
[slides_2015 (pdf)]
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Week
6
GLM
[slides_2015 (pdf)]
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Week
7
[Guest Lecture by Prof. E. Rolls]
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Week
8
Multi-Comparison Corrections
[slides_2015 (pptx)]
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Week
9
Applications
[slides_2015 (pptx)]
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Week 10 Final Seminar: assignment help
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Assignment: download .docx file here Assignment 2015; Datasets for question 1 (d); Datasets for question 3; Datasets for question 5;
Many general Comp Biol textbooks have some sections devoted to some of the material covered in this course. Furthermore, there are many on-line materials such as lecture notes, applets etc. on Comp Biol. Here is a link to Computational Biology Summer School in 2013.
[1] Huettel, Scott A., Allen W. Song, and Gregory McCarthy. Functional magnetic resonance imaging. Vol. 1. Sunderland, MA: Sinauer Associates, 2004.
[2] SPM8 Manual, matlab online help
[3] Efron, Bradley (2010). Large-Scale Inference. Cambridge University Press. ISBN 978-0-521-19249-1.