Educational responsabilities
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since 2017
Head of the Bioinformatics Master Mention of the University of Nantes
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since 2012
Co-head of the Master of Bioinformatics of the University of Nantes (First and Second Year Degrees),
after its refactoring into a training program integrating bioinformatics, informatics and biostatistics
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2005-2012
Creator and Head of the Master of Bioinformatics of the University of Nantes (First and Second Year Degrees)
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2001-2002
Creator and Head of the Second Year Master Degree (D.E.S.S.) of Bioinformatics of the University of Clermont-Ferrand 2
Educational responsabilities
-
since 2017
Head of the Bioinformatics Master Mention of the University of Nantes
-
since 2012
Co-head of the Master of Bioinformatics of the University of Nantes (First and Second Year Degrees),
after its refactoring into a training program integrating bioinformatics, informatics and biostatistics
-
2005-2012
Creator and Head of the Master of Bioinformatics of the University of Nantes (First and Second Year Degrees)
-
2001-2002
Creator and Head of the Second Year Master Degree (D.E.S.S.) of Bioinformatics of the University of Clermont-Ferrand 2
Christine Sinoquet
My current research interests include artificial intelligence, machine learning, data mining and combinatorial optimization.
Past Topics
Molecular phylogeny
Correction of dissimilarity matrices, for phylogeny reconstruction, continuation of a collaboration with the LIMOS, at Clermont-Ferrand
Research of motifs common to several sequences
studied during my PhD thesis and further
Comparative genomics
Identification of putative strong transcription promoters, a collaboration with the Biotechnology Laboratory of the University of Nantes
Modeling of gene regulatory networks
Model transformation and integration of temporal constraints,
a collaboration between two Computer Science Research Laboratories of Nantes (IRRCyN and LINA)
Inference of haplotypes from unphased genotypes
Inference of the complete information borne by each of two paired chromosomes (i.e. the phases), for all individuals in a population, relying on (unphased) genotypes
Large-scale inference of missing data, for genetical data
Exploitation of local similarities within groups of individuals, within a given population
Simulation of realistic genetical data
In GWASs, the statistical power of any novel proposal to detect susceptibility variants has to be assessed. For this purpose, one has to simulate genotypes and phenotypes under the hypothesis of association, that is with a dependence between some genetic marker(s) and the studied phenotype.
Recovery of epistatic patterns of susceptibility in complex diseases
Epistasy defines the case when some genetic markers have no individual effect on a given phenotype, whereas their combination has one.
Recent Topics
Modeling of dependences between event traces and multivariate time series from a cohort
The aim is to detect regime changes and to model the evolution dynamics within each regime in a semi-supervised context (i.e., with both known and unknown states).
Fast recognition of contextualized multidimensional patterns in sets of annotated multivariate time series
This topic is investigated to allow future health professionals to practice on a digital patient, in the simulated operating room, thanks to reactive scenarios. Each time the learner triggers a medical action, the digital patient's physiological parameters are modified based on the records of (real) observed patients most similar to the digital patient, in their recent multivariate trajectories. This process is iterated from action to action, under strong real-time constraints.
Synthetic representation to describe clusters of event trajectories
For instance, the challenge is to obtain succinct representations for clusters of patients described by temporal health-care data.
Consensus recommendation for a group
The challenge consists in selecting items to collectively satisty at best the preferences of a group of subjects.
Random forests with latent variables
This model was proposed to handle feature selection in highly correlated data.
Feature subset selection in high-dimensional settings
Focus on methods falling into the following categories: random forest, Bayesian inference, ant colony optimization, Markov blanket learning.
Modeling of high-dimensional and spatially correlated data
A novel class of probabilistic graphical models, the forest of latent tree models, was proposed, and a scalable learning algorithm was designed.
Advanced models in machine learning for advanced GWAS strategies
An association study aims at identifying associations between genetic markers (e.g. Single Nucleotide Polymorphisms - SNPs) and phenotype (e.g. a disease, plant resistance to a bioagressor). Genome wide association studies pose formidable challenges.