Machine Learning (ML) continues to be increasingly important to biological and medical research. Across these diverse fields, advances in dataset size and availability, ML algorithm competence and computational power are transforming modern science. Deep Learning (DL) has been instrumental to this progress, bringing the promise to radically transform human wellness and healthcare. One of the striking advantages of DL over classical ML is its natural ability to integrate heterogeneous datasets, as well as multiple sources of information, and resolve arbitrarily complex relationships.
In this workshop, some of the most recent state-of-the-art solutions of DL for Biomedicine and Computational Biology will be presented.
The PyTorch Deep Learning framework will be used, along with the fully fledged Python data science ecosystem (e.g. pandas, numpy, scikit-learn).
The tutorial is intended for researchers interested in exploring the latest ML/DL solutions for the Health and the Life Sciences; and for practitioners who wants to learn more about the PyTorch framework.
Proficiency with the main structures of the Python language is required, plus basic knowledge of statistical learning and computational biology is ideal, but not compulsory for the tutorial.
The supplementary code material is available at: