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Abstract: 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, I will provide an overview of TDA methods that complement current analytical approaches based on machine learning for precision medicine studies. I will introduce two popular techniques from TDA: the Persistent Diagram and Mapper graph, and will discuss how these techniques are effective, based upon the literature available where TDA has been applied in the context of precision medicine. Lastly, I very briefly present our ongoing work on how to integrate TDA with machine learning models to identify homogeneous subgroups of patients and predict clinical outcomes.
Mini-bio: Raquel is a BRC Lecturer in Statistical Learning at the Department of Biostatistics and health Informatics at King's College London. She is a mathematician and statistician, and holds a PhD in genetic epidemiology. Her research interests include the development of novel models based on Machine/Statistical Learning and Topological Data Analysis to identify clinical and genetic predictors of risk to complex disorders and response to treatment at the individual level, with main works on major depression, hypertension, cancer and schizophrenia.
Raquel is an active and dedicated lecturer, and leads and teaches regularly in introductory and advanced courses on statistics and machine learning for MSc and PhD students, in UK and abroad. She organises monthly seminars on Machine Learning and Prediction modelling and is co-lead for the Machine learning module in the MSc “Statistical Modelling and Health Informatics” at King’s. This has made her an effective advocate for machine learning and big data methods in biomedicine.
Twitter: @raqini