[Forum SIS] 2 seminars - July 19 - h 14.00 - seminar room, III floor, Dip. di Matematica, Politecnico di Milano

[Carlo Lauro]

1

Il giorno 16 Lug 2018, 11:36, alle ore 11:36, Alessandra Guglielmi <alessandra.guglielmi at polimi.it> ha scritto:
>Il giorno 19 luglio, alle ore 14.00, nell'aula seminari al terzo piano,
>
>Dipartimento di Matematica
>
>del Politecnico di Milano (via Bonardi, 9, Edificio La Nave, Campus 
>Leonardo, Milano)
>
>si terranno due seminari. I dettagli di seguito.
>
>Cordiali saluti
>
>Alessandra Guglielmi
>
>-- 
>Alessandra Guglielmi
>Dipartimento di Matematica - Politecnico di Milano
>tel ++39.02.23994641
>e-mail:alessandra.guglielmi at polimi.it
>
>
>############################################
>
>Annalisa Cadonna, Institute for Statistics and Mathematics, WU Vienna
>
>Title: Spectral Density Estimation for Multiple Time Series
>
>The problem of estimating the spectral density function arises
>naturally 
>in fields where information about frequency behavior is relevant and 
>several related signals are recorded concurrently. For example, 
>multichannel electroencephalography (EEG) records measurements of 
>electrical potential fluctuations at multiple locations on the scalp of
>
>a subject. I will present a hierarchical Bayesian modeling approach to 
>spectral density estimation for multiple time series, where the 
>log-periodogram of each series is modeled as a mixture of Gaussian 
>distributions with frequency-dependent weightsand mean functions. The 
>implied model for each log-spectral density is a mixture of mean 
>functions with frequency-dependent weights. In addition to
>accommodating 
>flexible spectral density shapes, a practical important feature of the 
>proposed formulation is that it allows for ready posterior simulation 
>through a Gibbs sampling algorithm with closed form full conditional 
>distributions for all model parameters. I will show results for 
>multichannel electroencephalographic recordings, which provide the key 
>motivating application for the proposed methodology, and present some 
>extensions to non-stationary time series.
>
>
>
>Andrea Cremaschi, Oslo Centre for Biostatistics and Epidemiology
>(OCBE), 
>University of Oslo
>
>Title: A Bayesian model for the study of drug-drug interactions
>
>Recently, increasing effort is being devoted to the study of the 
>simultaneous administration of two drugs to the same kind of cell 
>culture. The outcome may be representative of synergistic or 
>antagonistic behaviour in terms of /cell viability/. A primary goal of 
>this paper is to establish a reference value representing the condition
>
>where the combined compounds do not interact, also called 
>/zero-interaction/ level. A number of approaches have been proposed
>that 
>define such quantity, and then compare it with the response obtained in
>
>combination, to establish the amount of interaction present in the 
>experiment at the moment of sampling. However, these approaches rely on
>
>different modelling assumptions on the concentration-response curve and
>
>on the mechanism of action, and may provide conflicting outcomes in 
>real-life situations. In order to overcome these issues, we interpret 
>the viability experiment in a probabilistic framework, by modelling 
>single-cell quantities of interest, and including the information 
>relative to different exposure conditions. In particular, we propose a 
>Bayesian regression framework for modelling the response surface of two
>
>drugs combined, and show its performance on a wide simulation study, as
>
>well as on a diffuse large B-cell lymphoma (DLBCL) high-throughput 
>screening dataset, comprising more than 400 drugs combined with the 
>standard-of-care drug Ibrutinib. Posterior estimates of the 
>zero-interaction level and of the interaction term are obtained via 
>adaptive MCMC algorithms.
>
>
>
>------------------------------------------------------------------------
>
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