A new automated and unsupervised algorithm, Risk Assessment Population IDentification, identifies risk-stratifying cells in single cell datasets with robust statistical and biological validation.
A supervised methodology for mutational signatures outperforms the current standard unsupervised approach revealing new tissue-dependent mutational signatures among which some for obesity.
Easy-to-use image analysis software enables single cell quantitation of cell types and division rates in complex 3D tissues including living Drosophila brains, mouse embryos and Zebrafish organoids.
A combination of signal processing and machine learning form a new approach to classify oscillatory coupling in single cycles without averaging over time and to capture cycle-by-cycle changes in coupling.
A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals.
CaImAn is an open-software package that equips the neuroscience community with a set of turnkey, fast and scalable solutions to pre-processing problems arising in single cell calcium imaging data analysis.
SARS-CoV-2 receptor ACE2 is expressed in nasal olfactory epithelia, tongue keratinocytes and small intestine enterocytes, connected with the COVID-19 patient phenotypes such as anosmia and diarrhea.
A biologically plausible learning rule enables recurrent neural networks to model the way in which neural circuits use supervised learning to perform time-dependent computations.
