Jean-Philippe Vert
Jean-Philippe Vert is giving two talks in Québec this week. The first one will be on his contributions of machine learning in the field of bioinformatics. His second talk will be about biological networks.
Talk 1
Some contributions of machine learning in bioinformatics
December third, 2008, 10h30, room PLT-2744 (Pavillon Andrien-Pouliot)
see http://www.ulaval.ca/Al/interne/plan/AdrienPouliot/reference.htm
Many problems in bioinformatics can be formulated as pattern recognition problems on non-standard objects, such as strings, graphs or high-dimensional vectors with particular structure. They have triggered many original developments in machine learning recently, in particular in the way data are represented and prior knowledge is introduced in the algorithm. In this talk I will present some of these developments through several examples in microarray data analysis, virtual screening, and inference of biological networks.
Talk 2
Inferring and using biological networks
Decembre fourth, 2008, 15h00, Amphithéâtre Fisher, CRCHUL Porte TR-54
see http://maps.google.ca/maps?f=q&hl=en&geocode=&q=46.769017,+-71.281705&ie=UTF8&ll=46.769017,-71.281705&spn=0.001911,0.003648&t=h&z=18&iwloc=addr&om=1
Protein and gene networks have become important tools recently to represent
and think about the complexity of biological processes involving interactions
among many molecules. Two problems must nevertheless be solved in order to
make them useful: (i) how to obtain large-scale networks of good quality, and (ii)
how to use them in order to extract useful information from, e.g., expression data.
In this talk I will address both issues with a machine learning approach.
Talk 1
Some contributions of machine learning in bioinformatics
December third, 2008, 10h30, room PLT-2744 (Pavillon Andrien-Pouliot)
see http://www.ulaval.ca/Al/interne/plan/AdrienPouliot/reference.htm
Many problems in bioinformatics can be formulated as pattern recognition problems on non-standard objects, such as strings, graphs or high-dimensional vectors with particular structure. They have triggered many original developments in machine learning recently, in particular in the way data are represented and prior knowledge is introduced in the algorithm. In this talk I will present some of these developments through several examples in microarray data analysis, virtual screening, and inference of biological networks.
Talk 2
Inferring and using biological networks
Decembre fourth, 2008, 15h00, Amphithéâtre Fisher, CRCHUL Porte TR-54
see http://maps.google.ca/maps?f=q&hl=en&geocode=&q=46.769017,+-71.281705&ie=UTF8&ll=46.769017,-71.281705&spn=0.001911,0.003648&t=h&z=18&iwloc=addr&om=1
Protein and gene networks have become important tools recently to represent
and think about the complexity of biological processes involving interactions
among many molecules. Two problems must nevertheless be solved in order to
make them useful: (i) how to obtain large-scale networks of good quality, and (ii)
how to use them in order to extract useful information from, e.g., expression data.
In this talk I will address both issues with a machine learning approach.
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