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Understanding and quantifying the uncertainty of predictions from COVID-19 pandemic models is essential to inform public health decision making. This issue presents one such examination using the influential CovidSim model.
Obtaining a consistent taxonomy of neuron types is challenging mainly because of the high dimensionality of the datasets. Coupled autoencoders are a step forward in achieving this goal.
The mechanisms facilitating evolutionary adaptation to future challenges are difficult to establish experimentally. Recent computational simulations of 200 cell populations indicate how evolution can hide useless genetic switches with capacity for later use.
The nature of biological networks still brings challenges related to computational complexity, interpretable results and statistical significance. Recent work proposes a new method that paves the way for addressing these issues when analyzing cancer genomic data.
Characterizing the aggregation of the peptide amyloid β is essential to better understand Alzheimer’s disease and to find potential targets for drug development. Deep neural networks make it possible to describe the kinetics of this peptide, opening the way for achieving this goal.
