A new automated and unsupervised algorithm, Risk Assessment Population IDentification, identifies risk-stratifying cells in single cell datasets with robust statistical and biological validation.
Simultaneous quantification of each of the main motor programs in the roundworm C. elegans yields new insights into the neural mechanisms that coordinate animal behavior.
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.
Text mining of complete EHRs for 14,017 diabetes patients and subsequent clustering led to phenotypically deep clusters, showing distinct glycemic profiles, comorbidities, and SNP association patterns.
anTraX is an algorithm and software package that facilitates automated analyses of insect social behavior in species and experimental settings that are not accessible with currently existing technology.
An algorithm for analysing brain connectivity data identifies cell types and connections in simple (C. elegans) and complex (mouse) nervous systems, and can even resolve structure and connectivity in a man-made microprocessor.
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.
The ability to rapidly stain for any combination of genes in intact tissue with automated quantification of transcripts in individual cells and spatial re-mapping affords new insights into lung biology, and will greatly accelerate progress in scientific and medical research.
