Machine learning methods like neural networks evolved in the 80's into essential bioinformatics tools, for example, to predict the secondary and 3D structure of proteins. These days the revolution in biology and medicine concerning molecular measurement techniques creates a huge amount of high-dimensional and noisy data. For such data, machine learning provides noise reduction, feature selection, structure extraction, classification and regression in order to manage, analyze, interpret, compare, and simulate the data. In this tutorial molecular measurement data, the corresponding biomedical questions, and machine learning approaches to analyze such data will be introduced.
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Hauptvortrag / Eingeladener Vortrag auf einer Tagung