Human gut resistomeSeminarComputational microbial genomics – Iqbal Zamin Thu 7 Mar 2019 02:00 pm Institut Pasteur Auditorium Francois Jacob – BIME (26)SeminarHuman gut resistome – Amine Ghozlane Thu 28 Feb 2019 02:00 pm Institut Pasteur Auditorium Francois Jacob – BIME (26)
Human gut resistomeSeminarDeciphering gene expression programs at single-cell resolution – Stein Aerts Fri 15 Feb 2019 11:00 am Institut Pasteur Jules Bordet room – METCHNIKOFF (67)
Single-cell technologies are revolutionising biology and provide new opportunities to trace genomic regulatory programs underlying cell fate. In this talk I will present several computational strategies for the analysis of single-cell RNA-seq and single-cell ATAC-seq data that exploit the genomic regulatory code, to guide the identification of transcription factors and cell states. I will illustrate these methods on several model systems, including the Drosophila brain. Finally I will discuss how single-cell analyses can contribute to cross-species comparisons of regulatory programs.SeminarBayesian matrix factorization for drug discovery and precision medicine – Yves Moreau Thu 31 Jan 2019 02:00 pm Institut Pasteur Auditorium Francois Jacob – BIME (26)
Matrix factorization/completion methods provide an attractive framework to handle sparsely observed data, also called âscarceâ data. A typical setting for scarce data are is clinical diagnosis in a real-world setting. Not all possible symptoms (phenotype/biomarker/etc.) will have been checked for every patient. Deciding which symptom to check based on the already available information is at the heart of the diagnostic process. If genetic information about the patient is also available, it can serve as side information (covariates) to predict symptoms (phenotypes) for this patient. While a classification/regression setting is appropriate for this problem, it will typically ignore the dependencies between different tasks (i.e., symptoms). We have recently focused on a problem sharing many similarities with the diagnostic task: the prediction of biological activity of chemical compounds against drug targets, where only 0.1% to 1% of all compound-target pairs are measured. Matrix factorization searches for latent representations of compounds and targets that allow an optimal reconstruction of the observed measurements. These methods can be further combined with linear regression models to create multitask prediction models. In our case, fingerprints of chemical compounds are used as âside informationâ to predict target activity. By contrast with classical Quantitative Structure-Activity Relationship (QSAR) models, matrix factorization with side information naturally accommodates the multitask character of compound-target activity prediction. This methodology can be further extended to a fully Bayesian setting to handle uncertainty optimally, and our reformulation allows scaling up this MCMC scheme to millions of compounds, thousands of targets, and tens of millions of measurements, as demonstrated on a large industrial data set from a pharmaceutical company. We also show applications of this methodology to the prioritization of candidate disease genes and to the modeling of longitudinal patient trajectories. We have implemented our method as an open source Python/C++ library, called Macau, which can be applied to many modeling tasks, well beyond our original pharmaceutical setting. https://github.com/jaak-s/macau/tree/master/python/macau.