Algorithms in bioinformatics

• understand the details of the algorithms commonly used in bioinformatics.
• develop computer programs implementing these algorithms.
• identify which type of algorithm is best suited to describe a given biological problem.
• understand the concepts of data redundancy and homology reduction.
• develop bioinformatics prediction algorithms describing a given biological problem.
• implement and develop prediction tools using the following algorithms: Dynamic programming, Sequence clustering, Weight matrices, Artificial neural networks, and Hidden Markov models.
• design a project where a biological problem is analyzed using one or more machine learning algorithms.
• implement, document and present the course project.

The course will cover the most commonly used algorithms in bioinformatics. Emphasis will be on the precise mathematical implementation of the algorithms in terms of functional computer programs. During the course, biological problems will be introduced and analyzed with the purpose of highlighting the strengths and weaknesses of the different algorithms. The following topics will be covered:

Dynamic programming: Needleman-Wunsch, Smith-Waterman, and alignment heuristics
Data redundancy and homology reduction: Hobohm and other clustering algorithms
Weight matrices: Sequence weighting, pseudo count correction for low counts, Gibbs sampling, and Psi-Blast
Hidden Markov Models: Model construction, Viterbi decoding, posterior decoding, and Baum-Welsh HMM learning
Artificial neural networks: Architectures and sequence encoding, feed-forward algorithm, back propagation and deep neural networks

The course will consist of lectures, discussion sessions and computer exercises, where the students will be introduced to the different algorithms, their implementation and use in analyzing biological problems. In the end of the course, the student will work on a group project were one or more of the algorithms introduced in the course are applied to analyze a biological problem of interest. The project report shall be written as a research paper.

22111/22110, or equivalent. Able to solve bioinformatic problems in a structured manner using simple programs and scripts in Unix environment, for example during project work.

Lectures, discussions, exercises and project

Minimum: 10, Maximum: 50.

Please be aware that this course has a minimum requirement for the number of participants needed, in order for it to be held. If these requirements are not met, then the course will not be held. Furthermore, there is a limited number of seats available. If there are too many applicants, a pool will be created for the remainder of the qualified applicants, and they will be selected at random. You will be informed 8 days before the start of the course, whether you have been allocated a spot.