Bioinformatics
Institute of Computer Science
University Freiburg
de

Software

Seminar: Machine Learning in Life Science
The amount of available biological data is growing at an unprecedented rate. In order to make an effective use of all these data one needs an efficient and practical information management but also and foremost tools for the extraction and distillation of useful and meaningful knowledge. This latter aspect is currently one of the main challenges in computational biology.
In this seminar we will review the principal machine learning methods that can be used to model and give insights on underlying phenomena in several biological domains.


Links to the course pages: [2011], [2012]
Course: Machine Learning in Life Science
The amount of available biological data is growing at an unprecedented rate. In order to make an effective use of all these data one needs an efficient and practical information management but also and foremost tools for the extraction and distillation of useful and meaningful knowledge. This latter aspect is currently one of the main challenges in computational biology.
The aim of this course is to introduce the basic techniques and types of machine learning models employed in modern molecular biology research.
The course will maintain a double perspective: from the biological point of view we consider problems in the domains of genomics, proteomics, systems biology and biological literature information mining; from the machine learning point of view, we consider questions such as the underlying assumptions in predictive models, the quality assessment problem, the design choices for supervised and unsupervised models.


Links to the course pages:
Textbook: The Elements of Statistical Learning (2nd edition) by Hastie, Tibshirani and Friedman (2008). Springer-Verlag
Course: Bioinformatics II