Advances in biological and medical technologies drive continuous generation of large amounts of biomedical Big Data. European Nucleotide Archive stores 260 million sequences comprising 339 trillion nucleotides. This will double in less than 3 years if the current rate of growth is sustained! Given the exponential progress in sequencing technology the increase will only get steeper, entailing an intensified demand for experts in NGS data analysis. Big Data requires applying new solution to leverage its potential. Machine Learning (ML) is the answer to the increased complexity of research problems in science, industry and in everyday life. It is our conviction that knowledge of the ML techniques is a crucial skill every data scientist should acquire throughout their training.
For above reasons, #NGSchool2019: Machine Learning for Biomedicine will be focused on Machine Learning (ML) and its application in Bioinformatics & NGS Data Analysis as well as personalised medicine. We will cover the following subjects:
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Introduction to Linux, programming (R and Python) and statistics
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Tools for efficient and reproducible research
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Modern 
and 
libraries/packages for biomedical data science
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Deep learning in long read sequencing data analysis
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Statistical and probabilistic analysis of biomedical data
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Integration of genomics data using ML for understanding gene regulation in its three dimensional context
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Quality control and typical mistakes of a beginner ML user
