Phylogenetic analyses of the four core virion proteins support a new evolutionary model for the origin of the main groups of eukaryotic viruses in the kingdom Bamfordvirae.

Machine learning models of coordinated hippocampal ensemble activity during sharp wave ripple activity encode structure that mirrors the place cell map expressed during exploration, and enable a new paradigm for analyzing and understanding this offline activity.

A fly's locomotion can be decomposed into a small number of locomotor features which can be modulated by odors and other sensory information.

A stochastic model of locomotory control in C. elegans based on an extensive new set of tracking data can explain and predict effects of ablations and mutations on behavior.

A general machine learning scheme for integrating time-series data from single-molecule experiments and molecular dynamics simulations is proposed and successfully demonstrated for the folding dynamics of the WW domain.

Protein language deep learning models can quickly and accurately translate amino acid sequences into profile hidden Markov models or a structure alphabet, dramatically improving remote homology search sensitivity without compromising space or time efficiency.

Over 7,000 novel translated sequences have been identified from human cells, including several hundred in annotated noncoding RNA, pseudogenes and de novo assembled transcripts.

For analyzing time-dependent patch-clamp or patch-clamp fluorometry data of ion channels in terms of Markovian models, the superiority of Bayesian filtering with respect to traditional deterministic approaches is demonstrated enabling more reliable quantification of the parameters.

Computational reaction representations and profile hidden Markov model searches of metagenomics databases can be harnessed to accurately predict bacterial species and enzyme sequences responsible for biotransformations in the human gut microbiome.

Evidence for neuromodulatory control of flexibility in human neural models.