With the higher availability of massive datasets online, machine learning has been re-discovered as a powerful tool to use computational methods to identify data trends that would be unnoticeable by humans otherwise. In this short workshop, we will explore what researchers actually mean when they talk about machine learning, and learn the best practices to be performed when applying machine learning to biomedical data. We will see what common mistakes can happen during a machine learning analysis and how to avoid them, and how to detect over-optimistic results.
Dr. Davide Chicco is a scientific research associate at the Institute of Health Policy, Management and Evaluation at the University of Toronto where he works on machine learning and computational statistics applied to healthcare data. He received his PhD degree in computer engineering at Politecnico di Milano university (Milan, Italy) and then has worked as a postdoctoral researcher at the Princess Margaret Cancer Centre in Toronto. Dr. Chicco has worked on applications of machine learning to bioinformatics, epigenetics, and healthcare data and is the author of the article “Ten quick tips for machine learning in computational biology” (BioData Mining, 2017), which became well-known in the bioinformatics community.
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