BioSB

Target audience The course is aimed at PhD students with a background in bioinformatics, computer science or a related field; a working knowledge of basic statistics and linear algebra is assumed. The BioSB course “Pattern recognition” and the ASCI course “Advanced pattern recognition” (a1) discuss many of the tools used in this course, but it is not required to have followed these. Prior… Continue reading →

The amount of Life Science data available in the public domain is a vast and growing resource for bioinformatics research. There are over 20 million papers in PubMed and over 1600 biological databases. In many cases finding and applying the information from these resources is far from trivial. Following this course will show you techniques for working with these distributed… Continue reading →

The BioSB research school and partner LUMC are organizing a 3-day course on RNA-seq data analysis. This is an advanced course for people with experience in NGS. The course will consist of seminars and hands-on command line, Galaxy and R practicals and will cover the analysis pipelines for differential transcript expression and variant calling. Examples will be taken from human… Continue reading →

In many different problems from bioinformatics and systems biology (e.g. multi parameter estimation, reverse engineering of gene networks, multi-alignment problem, 3D structure prediction etc.) various optimisation methods are applied. In this course you will get acquainted with the underlying mathematics of optimization and with a selection of local and global optimization methods. In addition, throughout this course a great variety… Continue reading →

In this Quantitative and Predictive Modelling course the participants learn how to describe the dynamic behaviour of biological systems and to integrate experimental data. Concepts of modelling in terms of differential equations are introduced via a great variety of case studies taken from diverse practices. The course offers a math refresher to help those participants who are not (yet) involved… Continue reading →

In the right hands, protein structures are a ‘power’ful tool to answer bio-molecular questions. Knowledge of the structure is a pre-requisite for rational drug design, for biotechnology, for chemical biology, and for answering a whole series of biomedical questions. In this course we will discuss the ‘production’ of protein structures by NMR, X-ray, and homology modelling. These methods all have… Continue reading →

This is a one-day introduction to the possibilities of the statistical calculation environment R for those who would like to use it, or have already started using R for statistical data analysis but would like to obtain a bit more background. We will start with a lecture in which the philosophy behind the R-language will be explained. An understanding of… Continue reading →

After having followed this course, the student has a good understanding of basic pattern recognition and machine learning techniques and is able to recognize what method is most applicable to data analysis problems (s)he encounters in bioinformatics applications. Date: September 25-29, 2017 Target audience: The course is aimed at PhD students with a background in bioinformatics, computer science or a related… Continue reading →

This is a one-day introduction to the possibilities of Matlab as a programming environment for simulation and analysis of biological models. We will start with a simple introduction of the basics of Matlab (matrices, vectors, manipulation of matrices and presentation of data). Having obtained some basic programming knowledge (writing of simple scripts and functions in Matlab), we will move on… Continue reading →

This is a one-day introduction to the possibilities of Matlab as a programming environment for simulation and analysis of biological models. We will start with a simple introduction of the basics of Matlab (matrices, vectors, manipulation of matrices and presentation of data). Having obtained some basic programming knowledge (writing of simple scripts and functions in Matlab), we will move on… Continue reading →