In the life sciences, data are collected and stored at an ever increasing rate. In medicine, patient records are now routinely collected. In bioinformatics, one of the fastest growing areas of research in biology, large amounts of data are being generated that are difficult to interpret. This is exemplified by the human genome project, which aims to systematically identify and characterize the structure, function and regulation of human genes, in particular those with medical relevance.
As the amount of data collected grows, the need to analyse the data and extract useful information from it becomes more and more pressing. Data mining methods, which encompass statistical and machine learning methods, can be used to this end.
The seminar gave an introduction to selected data analysis methods from statistics and machine learning, as well as illustrative case studies of using these methods to analyse life sciences data. The latter include structure-activity prediction for toxicity and drug design, predicting protein structure and gene function from sequence data, and medical applications, such as selecting embryos for in-vitro fertilization. They will be presented by internationally recognized data analysis experts with practical experience in the abovementioned fields.
The seminar was intended for researchers and other professionals in the life sciences, including bioinformatics, biology, chemistry, medicine, molecular biology, and related fields, whose work requires the analysis of data. The seminar language was English.
The topics covered were as follows:
The lectures were given by
The seminar was attended by 18 participants, six of them from two institutions in the ILPnet2 End-User-Club: The National Institute of Chemistry (Ljubljana) and DIALOGIS (Bonn). Besides the two lecturers from the UK, there were two participants from Germany.
The seminar was financially supported by the EU funded projects ILPnet2: Network of Excellence in Inductive Logic Programming and Sol-Eu-Net: Data Mining and Decision Support for Business Competitiveness.