Dr
Christine Keribin
(Université Paris Sud)
22/06/2018 09:30
The Latent Block Model (LBM) designs in a same exercise a clustering of the rows and the columns of a data array. Typically the LBM is expected to be useful to analyze huge data sets with many observations and many variables. But it encounters several numerical issues with big data set: maximum likelihood is jeopardized by spurious maxima and selecting a proper model is challenging since...
Prof.
Agathe Guilloux
(Université d'Évry Val d'Essonne)
22/06/2018 10:00
We introduce a supervised learning mixture model for censored durations (C-mix) to simultaneously detect subgroups of patients with different prognosis and order them based on their risk. Our method is applicable in a high-dimensional setting where datasets contain a large number of biomedical covariates.
To address this difficulty, we penalize the negative log-likelihood by the Elastic-Net,...
Dr
Jean-Patrick Baudry
(LSTA)
22/06/2018 11:00
High-dimensional flow and mass cytometry allow to measure the expression of several proteins on tens of thousands of immune cells of a patient. A common task is to predict patients disease status. This can be done based on characteristics of the cells clusters of each patient. Hence the need for clustering methods.
Some constraints make this problem challenging. The clusters of cells need to...
Dr
Cathy Maugis-Rabusseau
(IMT / INSA Toulouse)
22/06/2018 11:30
Complex studies of transcriptome dynamics are now routinely carried out using RNA sequencing (RNA-seq). A common goal in such studies is to identify groups of co-expressed genes that share similar expression profiles across several treatment conditions, time points, or tissues. These co-expression analyses can in fact serve a double purpose: (1) as an exploratory tool to visualize...