High-throughput experimental methods, such as DNA and protein microarrays, have become ubiquitous and indispensable tools in biology and biomedicine, and the number of high-throughput technologies is constantly increasing. They provide the power to measure thousands of properties of a biological system in a single experiment and have the potential to revolutionize our understanding of biology and medicine. However, the high expectations on high-throughput methods are challenged by the problem to statistically model the wealth of data in order to translate it into concrete biological knowledge, new drugs, and clinical practices. In particular, the huge number of properties measured in high-throughput experiments makes statistical model selection and assessment exigent. To use high-throughput data in critical applications, it must be warranted that the models we construct reflect the underlying biology and are not just hypotheses suggested by the data. We must furthermore have a clear picture of the risk of making incorrect decisions based on the models. The rapid improvements of computers and information technology have opened up new ways of how the problem of model selection and assessment can be approached. Specifically, eScience, i.e. computationally intensive science that is carried out in distributed network envi- ronments, provides computational power and means to efficiently access previously acquired scientific knowledge. This thesis investigates how we can use eScience to improve our chances of constructing biologically relevant models from high-throughput data. Novel methods for model selection and assessment that leverage on computational power and on prior scientific information to "guide" the model selection to models that a priori are likely to be relevant are proposed. In addition, a software system for deploying new methods and make them easily accessible to end users is presented.