Presented By: DCMB Seminar Series
CCMB / DCMB Weekly Seminar Series
Jean-Philippe Vert (of Google Brain in Paris), "Computational analysis of tumour heterogeneity, from bulk to single-cell genomics"
Abstract:
Understanding intra-tumour heterogeneity (ITH), in particular identifying the presence of subclonal populations of cancer cells that may respond differently to treatments, is key to support precision medicine approaches. Capturing ITH from genomic measures raises however a number of computational challenges. In this talk I will present CloneSig, a method to infer ITH from "bulk" genomic data, in particular whole-exome sequencing data, and capture changes in mutational processes active in different subclones. I will then discuss the promises of single-cell genomics and some challenges it raises, in particular to transform raw count data into useful representations, integrate heterogeneous modalities, and learn gene regulation.
Short bio: Jean-Philippe Vert has been a research scientist at Google Brain in Paris and adjunct researcher at PSL University Mines ParisTech since 2018. He graduated from Ecole Polytechnique and holds a PhD in mathematics from Paris University. He was research professor and the founding director of the Centre for Computational Biology at Mines ParisTech from 2006 to 2018, team leader at the Curie Institute on computational biology of cancer (2008-2018), visiting scholar at UC Berkeley (2015-2016), and professor at the department of mathematics of Ecole normale supérieure in Paris (2016-2018).
His research interest concerns the development of statistical and machine learning methods, particularly to model complex, high-dimensional and structured data, with an application focus on computational biology, genomics and precision medicine. His recent contributions include new methods to embed structured data such as strings, graphs or permutations to vector spaces, regularization techniques to learn from limited amounts of data, and computationally efficient techniques for pattern detection and feature selection.
He is also working on several medical applications in cancer research, including quantifying and modeling cancer heterogeneity, predicting response to therapy, and modeling the genome and epigenome of cancer cells at the single-cell level.
https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
Understanding intra-tumour heterogeneity (ITH), in particular identifying the presence of subclonal populations of cancer cells that may respond differently to treatments, is key to support precision medicine approaches. Capturing ITH from genomic measures raises however a number of computational challenges. In this talk I will present CloneSig, a method to infer ITH from "bulk" genomic data, in particular whole-exome sequencing data, and capture changes in mutational processes active in different subclones. I will then discuss the promises of single-cell genomics and some challenges it raises, in particular to transform raw count data into useful representations, integrate heterogeneous modalities, and learn gene regulation.
Short bio: Jean-Philippe Vert has been a research scientist at Google Brain in Paris and adjunct researcher at PSL University Mines ParisTech since 2018. He graduated from Ecole Polytechnique and holds a PhD in mathematics from Paris University. He was research professor and the founding director of the Centre for Computational Biology at Mines ParisTech from 2006 to 2018, team leader at the Curie Institute on computational biology of cancer (2008-2018), visiting scholar at UC Berkeley (2015-2016), and professor at the department of mathematics of Ecole normale supérieure in Paris (2016-2018).
His research interest concerns the development of statistical and machine learning methods, particularly to model complex, high-dimensional and structured data, with an application focus on computational biology, genomics and precision medicine. His recent contributions include new methods to embed structured data such as strings, graphs or permutations to vector spaces, regularization techniques to learn from limited amounts of data, and computationally efficient techniques for pattern detection and feature selection.
He is also working on several medical applications in cancer research, including quantifying and modeling cancer heterogeneity, predicting response to therapy, and modeling the genome and epigenome of cancer cells at the single-cell level.
https://umich-health.zoom.us/j/93929606089?pwd=SHh6R1FOQm8xMThRemdxTEFMWWpVdz09
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