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Pres blum persyvact_04032015
The Persyvact project aims to build tools for analyzing high-dimensional and structured data using cutting-edge mathematical and algorithmic developments. The project involves researchers from three labs (TIMC-IMAG, gipsa-lab, LJK) with a total of 21 people. The project organized several scientific events and is supervising three PhD theses applying machine learning techniques to genomic data analysis problems like imputation, phasing, and clustering. If selected, Persyvact2 would continue this work on structured models and algorithms for high-dimensional complex health data analysis.
Pres blum persyvact_04032015
- 1. Exploratory project Persyvact Buildingtools for analyzing high dimensional and structured data. The tools proposed by Persyvact are based on cutting-edge mathematical and algorithmic developments. TIMC-IMAG Co-PI:M Blum 4 people gipsa-lab Co-PI: L Condat 8 people LJK Co-PI: M Clausel 9 people Kick-off meeting Persyvact, 16 people 20 septembre 2013, château de la commanderie
- 2. Organization of 8scientific events March 31-April 1 Spring school on machine learning with the EA Khronos April 2-3 Statlearn 2015 in Grenoble 2014 Seminar on machine learning for personalized genomics Workshop on graphical models Astrostatistics in Grenoble 2013 Seminar on Bayesian approaches Workshop on extremes and copulas Interdisciplinary meeting on Human Color Perception
- 3. TIMC-IMAG M Blum O François gipsa-lab OMichel L Condat J Chanussot LJK J Mairal S GirardM Clausel V Perrier PhD cosupervision Machine learning for genomic imputation and phasing Spatially regularized NMF for genetic application Modeling of anisotropic textures with monogenic wavelets Sequential data learning
- 4. Data X ingenomics Locus 1 Locus 2 Locus 3 Indiv 1 1 0 2 Indiv 2 0 2 0 Indiv 3 0 0 0 Indiv 4 0 1 1 Indiv 5 1 1 1 • 3 billion base pairs in the human genome • 1 000- 5 000 Euros for whole genome sequencing (millions of loci) Local correlation Spatialcorrelation
- 5. Spatially regularized NMFfor genetic application PhD Kevin Caye (supervision O François, O Michel) • Clustering (Vincent Delerm) « T'es dans la catégorie de celles qui lisent Bukowski en trouvant super naze de mettre les gens dans des cases. » • In genetics, we rather estimate admixture coeffients that correspond to the respective contributions of the parental populations to the admixed individuals.
- 8. Machine learning forgenomic imputation and phasing PhD Thomas Dias-Alves (supervision M Blum, J Mairal) Maternal copy 1100110 Paternal copy 0000100 11002x0 Phasing Application médicale pour l’étude du déterminisme génétique des maladies Haplotypes Genotypes
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- 10. An example ofphasing
- 11. Perspectives Project-team Persyvact2 forthe ongoing call “Structured models and algorithmic methods for the analysis of complex data in high dimension. Application for health monitoring. ”