Where possible I shall post lecture notes the evening after the lecture, so that (inevitable) mistakes spotted in the class can be corrected. Please do not email me immediately after the class asking for the lecture slides.
Additional reading will be posted before or after the lecture as appropriate.
2010 notes are available for reference
For further material on basic probability and distributions, you may find
the following texts useful: Grimmet and Stirzaker, Probability and random processes (OUP), C.M. Bishop, Pattern Recognition and Machine Learning, Springer, particularly section 1.2. You should be able to find these books in the library and you may be able to find pdfs for them on the web. A great book which is available on the web is Information Theory, inference and learning algorithms
by David Mackay. Chapter 23 contains a review of useful distributions, including the ones mentioned in the lecture.
Some exercises on the fundamentals of probability and Bayesian inference can be found here
Lecture 3 (26 Jan 2011) Microarray Technologies - Guest lecture from Donald Dunbar
The lecture introduces the basics of microarray technology and data analysis. Further reading to complement the lecture is recommended: a good starting point could be wikipedia's detailed page on DNA microarrays
. For a (much) more detailed treatment, you may wish to consuld Baldi and Wesley Hatfield, DNA Microarrays and Gene Expression, Cambridge University Press 2002.
This lecture introduces networks in biology and how they can be modelled statistically. A nice introductory review article (going in more depth than we do) is this
. Also of interest could be Terry Speed's lectures
at the Edinburgh summer school on statistical inference in computational biology in 2010 (in fact, these were my main inspiration).
The lab will introduce some of the features of the cytoscape visualisation tool for analysis of high throughput data in a network context. The slides contain a brief presentation about cytoscape functionalities and the task for the class.
This lecture introduces dynamical systems and how they can be useful in modelling certain situations in biology. A good reference in general for dynamical systems and much of machine learning is David Barber's new book, available here (this is much more detailed than we need). A very good discussion of HMMs in sequence analysis problems is given in Durbin et al's book, Biological sequence analysis (on the reading list).
Lecture 6 (23 Feb 2011) Guest Lecture from Dr Chris Larminie, GSK
This lecture introduced the uses of bioinformatic tools in drug discovery within an industrial R\& D environment. The lecture slides are not posted here but have been emailed to the students.
Lecture 7 (2 Mar 2011) ChIP-on-chip Technologies - Guest Lecture from Ian Simpson Slides
. This lecture introduced the fundamental technology behind ChIP-on-chip and ChIP-Seq, as well as the main data analysis tools. Further references and material can be found here
Lecture 8 (9 Mar 2011) Inferring Gene Regulatory Networks - Guest Lecture from Dirk Husmeier, BioSS
The two sets of slides can be found here
Tutorial (16 Mar 2011), solving together some simple questions in preparation for the exam
. Questions are available here