<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"> <HTML> <HEAD> <META HTTP-EQUIV="Content-Type" CONTENT="text/html; CHARSET=iso-8859-1"> <TITLE>ERC SMAC</TITLE> </HEAD> <BODY BGCOLOR="#FFFFFF" TEXT="#003300" LINK="#007700" VLINK="#FF7700"> <TABLE BORDER=0 CELLPADDING=1 CELLSPACING=10 WIDTH=100%> <tr> <th align=center><a href="http://erc.europa.eu"><img src="pic/erc_banner-vertical.jpg" border=none></a></th> <th align=center><h1 align="center">SMAC (2012-2017)<br><br>Statistical Machine Learning for Complex Biological Data</h1><br><h2 align="center"><i>Principal Investigator: <a href="http://cbio.mines-paristech.fr/~jvert">Jean-Philippe Vert</a></h2></th> </tr> </table> <p>SMAC is a research project funded by the <a href="http://erc.europa.eu">European Research Council (ERC)</a> and coordinated by <a href="http://cbio.mines-paristech.fr/~jvert">Jean-Philippe Vert</a>. This interdisciplinary project aims to <b>develop new statistical and machine learning approaches to analyze high-dimensional, structured and heterogeneous biological data</b>. We focus on the cases where a relatively small number of samples are characterized by huge quantities of quantitative features, a common situation in large-scale genomic projects, but particularly challenging for statistical inference. In order to overcome the curse of dimension we propose to exploit the particular structures of the data, and encode prior biological knowledge in a unified, mathematically sound, and computationally efficient framework. These methodological development, both theoretical and practical, will be guided by and applied to the inference of predictive models and the detection of predictive factors for prognosis and drug response prediction in cancer.</p> <h4><a href="2010proposal/DoW-SMAC.pdf">Detailed project description</a></h4> <h4>Team members</h4> <ul> <li><a href="">Jean-Philippe Vert</a> (principal investigator)</li> <li><a href="http://cbio.ensmp.fr/~ebernard">Elsa Bernard</a> (PhD)</li> <li><a href="http://cbio.ensmp.fr/~pchiche">Pierre Chiche</a> (PhD)</li> <li><a href="http://cbio.ensmp.fr/~ocollier">Olivier Collier</a> (Post-doc)</li> <li>Svetlana Gribkova (Post-doc)</li> <li><a href="http://cbio.ensmp.fr/~thocking">Toby Dylan Hocking</a> (PhD, now at Tokyo Institute of Technology)</li> <li><a href="http://cbio.ensmp.fr/~yjiao">Yunlong Jiao</a> (PhD)</li> <li><a href="http://cbio.ensmp.fr/~akhaleghi">Azadeh Khaleghi</a> (Post-doc)</li> <li>Beyrem Khalfaoui</li> <li>Marine Le Morvan</li> <li><a href="http://cbio.ensmp.fr/~mmoarii">Matahi Moarii</a> (PhD)</li> <li><a href="http://cbio.ensmp.fr/~erichard/">Emile Richard</a> (Post-doc, now at Stanford)</li> <li>Nino Shervashidze (Post-doc)</li> <li><a href="http://cbio.ensmp.fr/~nvaroquaux">Nelle Varoquaux</a> (PhD)</li> </ul> <h4>Events</h4> <ul> <li><a href="https://sites.google.com/site/smileinparis/">Statistical Machine Learning in Paris (SMILE in Paris)</a> seminar</li> <li><a href="http://cbio.ensmp.fr/~jvert/pcb12/">Paris Cancer Bioinformatics Workshop 2012</a> (October 4-5, 2012, Paris, France)</li> <li><a href="http://www.mlcb.org/previous/mlcb2012">Machine Learning in Computational Biology (MLCB 2012)</a> workshop (December 7, 2012, Lake Tahoe, USA)</li> <li><a href="http://www.mines-paristech.eu/Research-valorization/Gold-Mines/Research-latest-news/New-directions-in-computational-biology-15-5-13/">New Directions in Computational Biology</a> workshop (May 15, 2013, Paris, France)</li> <li><a href="http://mlsb.cc/">7th International Workshop on Machine Learning in Systems Biology (MLSB 2013)</a> (July 19-20, 2013, Berlin, Germany)</li> <li><a href="http://www.mlcb.org">Machine Learning in Computational Biology (MLCB 2013)</a> workshop (December 10, 2013, Lake Tahoe, USA)</li> </ul> <h4><a href="http://cbio.mines-paristech.fr/~jvert/publi">Publications</a></h4> <h4><a href="http://cbio.mines-paristech.fr/~jvert/software">Softwares</a></h4> <h4><a href="http://cbio.mines-paristech.fr/~jvert/teaching">Courses and tutorials</a></h4> <h4><a href="http://cbio.mines-paristech.fr/~jvert/talks">Presentations</a></h4> </BODY>