Category: q-bio.PE
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PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders arXiv:2502.04730v1 Announce Type: new Abstract: Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used to project phylogenetic trees into Euclidean space, but they are often sensitive to the choice of distance metric and may lack…
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Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks
Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks arXiv:2501.02043v1 Announce Type: new Abstract: Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to improve predictions of Susceptible-Infected-Recovered (SIR) models by incorporating human mobility…