The course will introduce the trainees to important concepts of data stewardship. We start with a general introduction covering the data life cycle, the FAIR principles and a definition of data stewardship and data stewards.

The first Helis Academy Omics Data Analysis course is a collaborative effort between Maastricht University and VIB in which you will learn about transcriptomics data analysis and multi-omics data integration.

This WIAS course will be given in Wageningen by prof. dr. Ulrike Müller from California State University Fresno. She is an active scientist who has published in and reviewed for high-impact journals (Nature, Science). The High-Impact writing course is not just another writing course; the focus is on high-impact writing rather than more general science communication.

This WIAS course will be given in Wageningen by prof. dr. Ulrike Müller from California State University Fresno. She is an active scientist who has published in and reviewed for high-impact journals (Nature, Science). The course ‘Survival guide to peer review’ provides the tools to address the comments of reviewers and editors in a professional way. The quality of a rebuttal can make the difference between a rejection and an acceptance.

This introductory course gives an overview of many statistical tools to analyse omics data. The course can be followed by researchers with a minimum or elementary background in quantitative data analysis.

In this 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.

In this course you will receive high profile lectures and practical training on the integrated use of molecular-omics technologies in personalized prevention, diagnoostics and treatment, to optimize your experimental and analysis strategies.

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.

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 course, you will be introduced to …..