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SeMoVi - May 14th 2014

This SemoVi was held in Grenoble, amphi 22 de l'IM2AG, 60 rue de la chimie, BP 53, 38041 Grenoble cedex 09.

14h00 - 15h00

Jean-Philippe Vert,
Mines ParisTech - Institut Curie - INSERM U900

More information on the Jean-Philippe Vert website

Machine learning for personalized genomics
15h00 - 15h30 Pause café  
15h30 - 16h30

Laurent Jacob, LBBE

Gérard Benoit, CGPHIMC

Statistical inference on large feature sets for biological sequences

Retinoic Acid Receptors: where and when to interact with DNA?

16h30 - 17h00 Discussion Générale  


Jean-Philippe Vert

Machine learning for personalized genomics

The genomic characterization of individual biological samples paves the way to personalized approaches to health care, such as deciding which treatment to give for cancer treatment, or predicting the toxicity of a chemical on an individual. I will discuss a few machine learning-based approaches that we developed to build such predictive models.


Gérard Benoit

Retinoic Acid Receptors: Where and when to interact with DNA?
Since the early days of molecular biology, understanding how genetic information is selectively expressed in living organisms is an issue that mobilizes a large part of the biologist community. As a consequence, the early concepts derived from prokaryotic models were progressively refine to propose a more complex and dynamic view of the intricate molecular mechanisms supporting transcriptional regulation in eucaryotic systems. The introduction of cistromic and transcriptomic analyses now offers the possibility to explore this issue at a the genomic level and shed new light on this fundamental biological mechanisms.


Laurent Jacob

Statistical inference on large feature sets for biological sequences

Several estimation problems in computational biology involving sequences lead to considering sets of features with size exponential in the sequence length. These feature sets correspond to combinations of sequence elements. They are typically too large to be explicitly described and manipulated, but can be represented as a set of paths on particular graphs. We use this implicit representation to make estimation possible.


Download poster here







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