ENBIS-23 Valencia Conference

Session

CONTRIBUTED Biostatistics and Machine Learning

13 Sept 2023, 08:30

2.13

Conveners

CONTRIBUTED Biostatistics and Machine Learning

• Bernard Francq (GSK)

61. Estimation of the Infection Rate of Epidemics in Multilayer Random Graphs: Comparing Classical Methods with XGBoost

Villő Csiszár (Loránd Eötvös University, Budapest)

13/09/2023, 08:30

Biostatistics

We address the problem of estimating the infection rate of an epidemic from observed counts of the number of susceptible, infected and recovered individuals. In our setup, a classical SIR (susceptible/infected/recovered) process spreads on a two-layer random network, where the first layer consists of small complete graphs representing the households, while the second layer models the contacts...

177. Transfer Learning across Biopharma Molecules, Scales and Phases Based on Hybrid Semi-Parametric Modeling

Hugo Santos (DataHow)

13/09/2023, 08:50

Quality by Design (QbD) guided process development is time and cost-effective only if knowledge is transferred from candidate to the next, from one scale to the other.

Nowadays, knowledge is shared across scales and candidates via technical risks evaluation. Though platform processes are widely used, this type of knowledge transfer is limited and every new candidate requires some degree of...

66. New Estimation Algorithm for More Reliable Prediction in Gaussian Process Regression: Application to an Aquatic Ecosystem Model

Amandine MARREL (CEA)

13/09/2023, 09:10

Machine learning

In the framework of emulation of numerical simulators with Gaussian process (GP) regression [1], we proposed in this work a new algorithm for the estimation of GP covariance parameters, referred to as GP hyperparameters. The objective is twofold: to ensure a GP as predictive as possible w.r.t. to the output of interest, but also with reliable prediction intervals, i.e. representative of its...