This year's mission:
AI for Computational Biology
The single-cell sequencing (SCS) is the most advanced omics technology, collecting the sequence information from individual cells and providing a higher resolution of the cellular structure and supporting a deeper investigation of the functions of an individual cell within its environment. Supported by a team of computational biologists, the WebValley Team will explore SCS data through unsupervised learning models and dimensionality reduction algorithms. In particular, advanced clustering techniques and UMAP manifold projections will be used to identify novel patterns possibly revealing different phenotypes yet undetected.
AI for predictive medicine
The scientific theme of WebValley 2019 is the application of Deep Learning methods to the integration of biomedical imaging, omics markers and clinical data for predictive health. In particular, the project will aim at widening the use of machine learning on massive health data out of the hospital context, e.g. for surveillance and point of care of disease.
In collaboration with medical experts, the WebValley team will study how to combine phenotype data, extensive clinical exams and biomarkers from portable imaging devices. Challenges will span from biomedical to machine learning and techs aspects, from reproducibility and interpretability of AI in health to practical skills in data science, including how to run privacy-aware deep learning in cloud.