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.
Building on simple unsupervised matrix factorization techniques, the seqNMF algorithm successfully recovers neural sequences in a wide range of simulated and real datasets.
DeepEthogram automatically classifies animal behavior videos into researcher-defined behaviors of interest, saving researcher time and enabling more detailed downstream analysis of behavior.
Model-based analyses of human behaviour and neural activity show that representations of concurrent task-sets emerge by merging together representations of individual stimulus-response associations that occur in temporal proximity.
Rare, virus-specific immune cells in human blood are automatically identified by machine learning algorithm T-REX and characterized for signature features needed for tracking and isolation.
DeepFly3D, a deep learning-based software, measures limb and appendage movements in tethered, behaving Drosophila and enables precise behavioral measurements during neural recordings, stimulation, and other biological experiments.
A supervised learning approach on a high-content genome-wide siRNA screen has identified 591 likely candidates for ciliopathies and facilitated in the discovery of KIAA0586 mutations in individuals with Joubert syndrome.
