Introduction to bioinformatics
published: Aug. 20, 2007, recorded: August 2007, views: 10260
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Watch videos: (click on thumbnail to launch)
I will start by giving a general introduction into Bioinformatics, including basic biology, typical data types (sequences, structures, expression data and networks) and established analysis tasks. In the second part, I will discuss the problem of predictive sequence analysis with Support Vector Machines (SVMs). I will introduce a series of kernels suitable for different analysis tasks. Furthermore I will discuss the basic data structures needed for large scale learning and how to combine kernels for heterogeneous data. In the third part, I will focus on Hidden Markov models and discriminative alternatives like Conditional Random Fields and Hidden Markov SVMs suitable for segmentation tasks frequently appearing in Bioinformatics. In the last part I will present three applications in greater detail: A large margin alignment algorithm, computational gene finding and the identification of polymorphisms from resequencing arrays.
Download slides: ratsch_gunnar_lecture1.pdf (1.4 MB)
Download slides: ratsch_gunnar_lecture2.pdf (780.4 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !