Research at CBIO is dedicated to the development of models and algorithms to analyze and understand biological and chemical data. We emphasize the use of probabilistic models and statistical machine learning. Our long-term objective is, through multiple collaborations, to contribute to the development of new therapies, in particular against cancer. In order to meet these goals our projects focus on the following topics:
- Computational biology and analysis of structured and heterogeneous biological data (such as genomic sequences, protein structures, gene networks...), applications in diagnosis, prognosis and personalized medicine
- Systems biology: gene network analysis and inference, integration of genomic data with gene networks.
- Data processing and analysis for new technologies in life science, in particular DNA chips, NGS, cell chips and cellular microscopy.
- Virtual screening: docking, machine learning on chemical databanks, chemogenomics.
- Theory and algorithms in statistical learning: learning on structured objects, theoretical analysis of algorithms.
- Machine learning for genome-wide association studies (collaboration SANOFI, 2016-2019)
- CRESTNETMETABO: New challenges in the regulatory network of neural crest early development (ANR 2015-2019)
- Hi-FISH: Systematic study of gene expression at the RNA single molecule level (ANR 2014-2018)
- ABS4NGS: Algorithms, bioinformatics and softwares for next-generation sequencing (ANR "Investments for the future" program, 2012-2016).
- SMAC: Statistical machine learning for complex biological data (ERC 2012-2017).
- MLPM: Machine Learning for Personalised Medicine. (EC-FP7 International Training Network, 2012-2016).
- RADIANT: Rapid development and distribution of statistical tools for high-throughput sequencing (EC-FP7 2012-2015).
- Systems Microscopy Network of Excellence (EC-FP7 2013-2015)
- TYRO3: TYRO3, a new therapeutic target for cancer (INCA 2012-2014).
- CRESTNET: Building regulatory networks in neural crest induction: integrative approaches in vivo and in stem cells (ANR 2012-2014).
- AP'ONCALYPSE: Validation of an immune signature predicting a therapeutic response to anthracyclines in breast cancer (ANR 2012-2013)
- Structured machine learning for microbiology: mass spectrometry and high-throughput sequencing (Collaboration with Biomerieux, 2011-2014).
- Integrated analysis of methylation profiles in breast cancers (Ligue contre le cancer, 2011-2014):
- [NADINE: Nanosystems for early diagnosis of neurodegenerative diseases] (EC-FP7 2010-2015)
- [CLARA: clustering in high dimension, algorithms and applications] (ANR 2009-2013).
- Development of algorithms and databases in cancer informatics (JSPS 2008-2010)
- MGA : Graphical models and applications (ANR 2007-2011)
- RAMIS : High-resolution microscopy for screening of anti-cancer drugs (2007-2011)
- ParTox : Monitoring the toxicity of nanoparticles (ANR 2007-2009)
- Inference and learning in dynamic graphical models, with applications in speech and bio-informatics (France-Berkeley fund, 2007-2009):
- Biotype : caracterization of prostate tumors by multiple technologies (MEDICEN 2007-2009)
- Machine learning for virtual screening (Carnot 2007-2009) :
- DSIR : algorithms for design of siRNA (Ligue contre le cancer, 2006-2007)
- Indigo : integrated highly sensitive fluorescence-based biosensors for diagnosis applications (EC-FP7 2005-2008)
- ESBIC-D : a European systems biology infrastructure for combating complex diseases (EC-FP7 2005-2007)
- NIH : detecting genomic relations among heterogeneous genomic datasets (NIH 2004-2007)
- Kernelchip : integration of gene expression data and gene regulatory networks for the study of cancerous tumors (CNRS 2004-2007)
- GemBio : analysis of anti-malaria drug effects on P. falciparum (Mines 2004-2007)
- BioClassif : statistical learning theory for structured and high-dimensional data (CNRS 2004-2006)
- Machine learning for virtual screening (ANVAR 2004-2006)
- iBioinfo : development of methods and bioinformatics tools to analyze cell chip data (CEA 2005-2006)
- Sakura : statistical and combinatorial analysis of biological networks (JSPS 2003-2005):
- Statistical learning for the analysis of transcriptome (CNRS 2003-2004)