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.
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.
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.
Combining high-resolution imaging with automated image segmentation and supervised machine learning achieves accurate cellular feature extraction and automated cell type recognition in a large-scale developmental process.