Les explications détaillées sur l'appel à propositions sont sur la page : https://www.ixxi.fr/appel-a-projets/appel-a-propositions-2024
Date limite de soumission : Vendredi 12 avril 2024 à 15:00 sur le site https://easychair.org/conferences/?conf=ixxi2024
N’hésitez pas à soumettre !
Systématiquement votre,
Le Bureau BioSyL
The amount and quality of biological datasets available is fast increasing. This holds at the molecular and cellular scales (gene sequence and expression data), but also at larger ones (populations and communities of cells, behavior of one or multiple animals). Making sense of these extraordinarily rich datasets calls for new inference and interpretation methods, and ultimately new theory. Since microscopic theories of biology may not provide insight on larger scales, data-driven approaches play an integral role in biophysics, for example guiding us to discovering new physical laws.
Various analysis approaches inspired by statistical physics and by machine learning are currently being developed. They range from fitting models to model-free analysis, and include supervised and unsupervised approaches. Recent advances in machine learning offer powerful new methods. For instance, some deep neural networks capture very well the rich structure of biological sequence data. Physics-based concepts play important parts in these analysis approaches, including deep learning ones. In turn, these models provide insight on biophysical phenomena.
This workshop will bring together scientists modeling biological data, performing and analyzing data-rich experiments, and those who are interested in developing, using and understanding new data analysis methods, such as deep learning. We will compare approaches, discuss successes and failures in data analysis, and reflect on future directions.
We invite you to apply to the workshop. We would also appreciate your help in encouraging other people who are active in this field to apply. Applications are now open.
Note that we cannot guarantee admission to the workshop. Admission to the workshop is granted not by the workshop organizers, but by the Admissions Committee of the Center. Because of the constraints imposed by the rest of the Aspen Center for Physics program, they are usually not able to admit everyone who applies.
Marianne Bauer, Anne-Florence Bitbol, Ilya Nemenman and Greg Stephens
The amount and quality of biological datasets available is fast increasing. This holds at the molecular and cellular scales (gene sequence and expression data), but also at larger ones (populations and communities of cells, behavior of one or multiple animals). Making sense of these extraordinarily rich datasets calls for new inference and interpretation methods, and ultimately new theory. Since microscopic theories of biology may not provide insight on larger scales, data-driven approaches play an integral role in biophysics, for example guiding us to discovering new physical laws.
Various analysis approaches inspired by statistical physics and by machine learning are currently being developed. They range from fitting models to model-free analysis, and include supervised and unsupervised approaches. Recent advances in machine learning offer powerful new methods. For instance, some deep neural networks capture very well the rich structure of biological sequence data. Physics-based concepts play important parts in these analysis approaches, including deep learning ones. In turn, these models provide insight on biophysical phenomena.
This workshop will bring together scientists modeling biological data, performing and analyzing data-rich experiments, and those who are interested in developing, using and understanding new data analysis methods, such as deep learning. We will compare approaches, discuss successes and failures in data analysis, and reflect on future directions.
We invite you to apply to the workshop. We would also appreciate your help in encouraging other people who are active in this field to apply. Applications are now open.
Note that we cannot guarantee admission to the workshop. Admission to the workshop is granted not by the workshop organizers, but by the Admissions Committee of the Center. Because of the constraints imposed by the rest of the Aspen Center for Physics program, they are usually not able to admit everyone who applies.
Marianne Bauer, Anne-Florence Bitbol, Ilya Nemenman and Greg Stephens
Nous recherchons des profils pour stage en bio-informatique sur le site de marcy l’étoile, profil du candidat niveau M2 et avec de l’expérience dans le secteur.
Les dates de stage sont de Janvier à Juin maximum,
Nous sommes activement en recherche de profils,
Voici le lien direct pour que les élèves postulent :
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Dossier de soumission ici
]]>Disciplines | Biology, Computer Science, Mathematics |
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Research fields |
Biology : Cancerology, Epidemiology, Evolution, Genomics, Transcriptomics Computer science: Data analysis, Bioinformatics, Mathematics: Statistical models, Biostatistics, Network modelling |
Supporting organisms | Université Grenoble-Alpes CNRS, VetAgro Sup, Grenoble-INP |
Geographical location | Domaine de la Merci (La Tronche) |
Lab |
Laboratoire TIMC – Translational Innovation in Medicine and Complexity |
Team leader |
Olivier François |
Webpage |
According to the geneticist Theodozius Dobzhansky (1973), nothing in biology makes sense except in the light of evolution. A current version of this quotation could add a light of genomics, the revolution of the 21rst century in the biological field. How not to see the dramatic progress and the implication of genomics for medicine, for the study of cancers, infections by pathogens, genetic or environmental diseases, associated treatments, etc.
By developing models and methods for the analysis of genomic data (in the broad sense), the MAGE group is part of the radical transformation of biological sciences and their fusion with computational sciences and mathematics. The team is strongly interdisciplinary, composed of biologists and mathematicians mastering the multiple facets of computational biology. The team objectives include four major areas of research, and new computational themes.
Research topics:
Analysis of genetic and epigenetic deregulations in cancers: characterization of inter- and intra-tumor heterogeneity (PI: Magali Richard).
Environmental genomics and epigenetics: mediation of environmental exposures, predictive molecular ecology (PI: Olivier François).
Evolution of microorganisms and metagenomics: competition and innovation in bacterial communities (PI: Antoine Frénoy).
Bioinformatics of NGS data: exome-seq, rip-seq, CNVs, call for genotypes and applications to male infertility (PI: Nicolas Thierry-Mieg).
Any other axis welcome!