A new user-intervention-free classification, using single-fluorescent markers, measures conventionally unmeasureable phenotypes in early stages during clathrin-mediated endocytosis.
An intelligent method is developed to morphologically classify platelet aggregates by agonist type, which potentially opens a window on novel clinical diagnostics and therapeutics of thrombotic disorders.
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.
An open-source user-friendly toolbox implementing machine learning for single-molecule FRET analysis enabling experts and non-experts to reproducibly provide dynamic structural biology insights.
Single units in a deep convolutional neural network trained for image classification develop shape selectivity that is similar to that found in the primate visual cortex.
Machine learning in conjunction with super-resolution imaging allows for the first time to quantitatively analyse large and heterogenous virus samples structure at a high throughput and specificity.
Machine learning and experimental tests of receiver bias identify signal components critical to correct species classification in guenons, linking face pattern diversity to selection for species discrimination.
Hippocampome.org is an online resource that provides free human- and machine-readable access to the comprehensive property-based classification of hippocampal neurons from 14,000 pieces of published experimental evidence.
