Seminars Details

17.02.2020

Helping machine learning to help us in personalized medicine

Prof. Dr. Julio Saez-Rodriguez, University of Heidelberg
ICB Seminar room at 11:30

Modern technologies allow us to profile in high detail biomedical samples at fastly decreasing costs New technologies are opening new data modalities, in particular to measure at the single cell level. Prior knowledge, and biological networks in particular, are useful to integrate this  data and distill mechanistic insight. This can help to interpret the result of machine learning or statistical analysis, as well as generate input features for these methods.

I will give an overview of our work on this area, where we have focused on transcriptomics and (phospho)proteomics to study signaling networks. Our tools range from a meta-resource of biological knowledge (Omnipath) to methods to infer pathway and transcription factor activities (PROGENy and DoRothEA, respectively) from gene expression and subsequently infer causal paths among them (CARNIVAL), to tools to infer logic models from phosphoproteomic and phenotypic data (CellNOpt and PHONEMeS). We have recently adapted these tools to single-cell data. I will illustrate their utility in cases of biomedical relevance, in particular to improve our understanding of cancer and to develop novel therapeutic opportunities.

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