arXiv Quantitative Biology @arxiv_bio@qoto.org

Evaluating undercounts in epidemics: response to Maruotti et al. 2022. (arXiv:2209.11334v1 [q-bio.PE]) http://arxiv.org/abs/2209.11334

Biophysical Modeling of SARS-CoV-2 Assembly: Genome Condensation and Budding. (arXiv:2209.11508v1 [physics.bio-ph]) http://arxiv.org/abs/2209.11508

BioKlustering: a web app for semi-supervised learning of maximally imbalanced genomic data. (arXiv:2209.11730v1 [q-bio.GN]) http://arxiv.org/abs/2209.11730

Semantic scene descriptions as an objective of human vision. (arXiv:2209.11737v1 [cs.CV]) http://arxiv.org/abs/2209.11737

A Driven Disordered Systems Approach to Biological Evolution in Changing Environments. (arXiv:2108.06170v2 [q-bio.PE] UPDATED) http://arxiv.org/abs/2108.06170

A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time. (arXiv:2202.09209v3 [q-bio.NC] UPDATED) http://arxiv.org/abs/2202.09209

Variational inference of fractional Brownian motion with linear computational complexity. (arXiv:2203.07961v4 [cs.LG] UPDATED) http://arxiv.org/abs/2203.07961

Configurational entropy, transition rates, and optimal interactions for rapid folding in coarse-grained model proteins. (arXiv:2205.05799v3 [cond-mat.soft] UPDATED) http://arxiv.org/abs/2205.05799

SGC: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks. (arXiv:2209.10545v1 [q-bio.GN]) http://arxiv.org/abs/2209.10545

Heterogeneity transforms subdiffusion into superdiffusion via ensemble self-reinforcement. (arXiv:2209.10599v1 [cond-mat.stat-mech]) http://arxiv.org/abs/2209.10599

Stochastic Kinetic Study of Protein Aggregation and Molecular Crowding Effects of Ab40 and Ab42. (arXiv:2209.10630v1 [physics.bio-ph]) http://arxiv.org/abs/2209.10630

Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation. (arXiv:2209.10634v1 [q-bio.NC]) http://arxiv.org/abs/2209.10634

Stochastic approach to study the properties of the complex patterns observed in cytokine and T-cells interaction process. (arXiv:2209.10698v1 [q-bio.PE]) http://arxiv.org/abs/2209.10698

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. (arXiv:2209.10702v1 [physics.chem-ph]) http://arxiv.org/abs/2209.10702

A Spatial-channel-temporal-fused Attention for Spiking Neural Networks. (arXiv:2209.10837v1 [cs.CV]) http://arxiv.org/abs/2209.10837

Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning. (arXiv:2209.10992v1 [eess.SP]) http://arxiv.org/abs/2209.10992

On the unknown proteins of eukaryotic proteomes. (arXiv:2209.11001v1 [q-bio.GN]) http://arxiv.org/abs/2209.11001

Simulation-based inference of Bayesian hierarchical models while checking for model misspecification. (arXiv:2209.11057v1 [stat.ME]) http://arxiv.org/abs/2209.11057

Predicting Drug-Drug Interactions using Deep Generative Models on Graphs. (arXiv:2209.09941v1 [q-bio.BM]) http://arxiv.org/abs/2209.09941

Development of theoretical frameworks in neuroscience: a pressing need in a sea of data. (arXiv:2209.09953v1 [q-bio.NC]) http://arxiv.org/abs/2209.09953