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Machine Learning and Network Analysis Approaches for Enabling Biomedical Research

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Host: Dr Shan He (

Abstract: The increasing growth of data available in biomedical research, together with the promise of precision medicine, are allowing significant advances in the understanding of complex biological systems and the prediction of patient outcomes. Despite such progress, applications in biomedical research and their translation to the clinic pose major and unique data science challenges. In this talk, I will outline some of such questions and requirements. Also I will outline research on the application of machine learning and network analysis approaches, independently or in combination, for addressing key predictive modelling challenges. Examples of advances will be discussed with an emphasis on the analysis of multiple types of “omics” data in cancer research.

Biography: Dr. Francisco Azuaje is a Principal Investigator at the Luxembourg Institute of Health (LIH). He leads the Computational Biomedicine Research Team and the Bioinformatics Platform at LIH . He previously held faculty positions as Lecturer and Reader in AI at Trinity College Dublin and the University of Ulster respectively. He was a Visiting Fellow at the US-NIH and is currently an Honorary Fellow of The University of Edinburgh. His top research priority is the development of computational tools for predicting clinically relevant outcomes based on the analysis of multiple types of biomedical data. His contributions to the field are reflected in part in his publication record, which includes more than 90 PubMed-indexed articles ( ).


This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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