Augmenting Machine Learning with Topological Data Analysis for precision

A talk by Dr Raquel Iniesta, BRC Lecturer in Statistical Learning for Precision Medicine.

Topological Data Analysis (TDA) is a recently emerged field offering promising tools to extract descriptors of the shape and structure of complex data. In this talk, Raquel provides an overview of TDA methods that complement current analytical approaches based on machine learning for precision medicine studies. She also introduces two popular techniques from TDA: the Persistent Diagram and Mapper graph, and discusses how these techniques are effective, based upon the literature available where TDA has been applied in the context of precision medicine.

Lastly, she very briefly presents her and her team's ongoing work on how to integrate TDA with machine learning models to identify homogeneous subgroups of patients and predict clinical outcomes.