** Machine Learning for bioinformatics and systems biology, 7-11 October 2019, Delft/Amsterdam, the Netherlands
Modern biology is a data-rich science, driven by our ability to measure the detailed molecular characteristics of cells, organs, and individuals at many different levels. Interpretation of these large-scale biological data requires the detection of statistical dependencies and patterns in order to establish useful models of complex biological systems. Techniques from machine learning are key in this endeavour. Typical examples are the visualization of single-cell RNA-seq data using dimensionality reduction methods, base calling for nanopore sequencing data using hidden Markov models and (recurrent) neural networks, and classification of high-throughput microscopy image data using convolutional neural networks.
In this one-week course, the foundations of machine learning will be laid out and commonly used methods for unsupervised (clustering, dimensionality reduction, visualization) and supervised (mainly classification) learning will be explained in detail. Methods will be illustrated using recent examples from the fields of systems biology and bioinformatics. Methods discussed in the morning lectures will be put into practice during the afternoon computer lab sessions.
** Algorithms for Genomics, 14-18 October 2019, Delft, the Netherlands
Comparative genomics aims to compare large sets of genomes in order to understand and explain differences in traits of an organism. Contemporary methods are powered by fundamental algorithms and data structures, which are efficient and scale to large data sets. A thorough understanding of these algorithms and data structures is necessary for advanced users and developers in this area. In addition, understanding how comparative genomics is developing is important to shape your own research.
In this 5-day course, we will cover genome analysis, variant analysis, and pangenomics. Core concepts, applications, and future trends will be discussed, with a focus on the algorithms and data structures underlying state-of-the-art methods. The course offers an engaging mix of lectures, paper discussions, hands-on tutorials, and a do-it-yourself project.
** Constraint-based modelling: Introduction & advanced topics, 25-29 November 2019, Leiden, the Netherlands
Constraint-based modeling is a powerful modeling methodology that is being used to model a diverse range of biological phenomena. These include both fundamental and applied questions relevant to biotechnology, microbiology and medicine. Central to constraint-based modeling is the use of genome-scale reconstructions that represent particular cellular functions as a biochemical reaction network. In this 5-day course, you will be introduced to:
the principles of constraint-based reconstruction and analysis (COBRA)
the underlying mathematical foundations of constraint-based modeling
methods for integration of omics data with constraint-based models
basic and advanced methods for interrogating models and interpreting results
open source COBRA software, including the COBRA Toolbox
standards for reconstruction and model sharing
example applications to biomedicine and biotechnology
The course is structured into both lectures and practical sessions so that theory can be illustrated with biologically motivated computational examples.
These courses are part of the BioSB research school course portfolio. The BioSB research school offers a wide variety of training courses that enable life scientists to apply modelling and computational approaches in addressing scientific challenges.
