
27825 
27825 Algorithms in bioinformatics    Danish title:  Algoritmer i bioinformatik  Language:   Point( ECTS )  5  Course type:  Ph.D. Taught under singlecourse student   
 January
 Location:  Campus Lyngby  Scope and form:  Lectures, discussions, exercises and project  Duration of Course:  3 weeks   Decide with teacher  Type of assessment:   Aid:   Evaluation:   Recommended prerequisites:  , 
General course objectives:
To provide the student with an overview and indepth understanding
of bioinformatics machinelearning algorithms. Enable the student
to first evaluate which algorithm(s) are best suited for answering
a given biological question and next implement and develop
prediction tools based on such algorithms to describe complex
biological problems such as immune system reactions, vaccine
discovery, disease gene finding, protein structure and function,
posttranslational modifications etc.Learning objectives:
A student who has met the objectives of the course will be able to:
 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.
 On a detailed level, the student will be able to 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.
Content:
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: NeedlemanWunsch, SmithWaterman, 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 PsiBlast
Hidden Markov Models: Model construction, Viterbi decoding,
posterior decoding, and BaumWelsh HMM learning
Artificial neural networks: Architectures and sequence encoding,
feedforward 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 including an indepth review of the field covered by
the project.Department:  27 Department of
Systems Biology  Home page:   Registration Sign up:  At CampusNet 
Last updated: 04. januar, 2016 No result







