Partiellement financé par Grenoble Alpes Data Institute ANR-15-IDEX-02
HALL DE RÉCEPTION, RDC du Bât. IMAG.
SALLE DE SEMINAIRE 2, RDC du Bât. IMAG.
This work offers a mapping and correlation of different characteristics (biological, physical and genetic) of selected cells lines to model their metastatic potential for improved diagnostic and prognostic capabilities. It aims providing the required tool to predict metastatic potential of a cell by means of physical properties; a method that is faster, cheaper and better suited for point-of-care applications.
For data processing, the needed statistical and computational classification and prediction methods require training with large-scale, heterogeneous, continuous (functional) and spatial datasets including high numbers of cells’ physical and biological properties. A very limited number of models are available for description, visualization, classification and prediction of quantitative data involving continuous (functional) heterogeneous data. Challenges are, in one hand, to use statistical (including functional classification, regression methods) tools able to compute in a non-costly way correlation among huge amounts of data, for training and accuracy prediction. A main issue when analyzing our massive data is to use statistical tools including functional classification, regression methods) able to compute in a non-costly way correlation among huge amounts of data and the ultimate goal of predicting the metastatic potential of cells by physical characterization of cells.
SALLE DE SEMINAIRE 2, RDC du Bât IMAG.
In the present talk, we will present some applications of the empirical processes for statistical tests and the nonparametric estimation. We first consider the QQ plot processes to perform statistical tests for change point problems. In the second part, we give applications for testing the independence between random variables (vectors). We finally discuss the copula representation in the regression setting.
AMPHI 1, Maison JEAN KUNTZMANN.