By centralizing many of the tasks associated with the upkeep of scientific software, SBGrid allows researchers to spend more of their time on research.
The single-cell eQTLGen consortium aims to pinpoint the cellular contexts in which disease-causing genetic variants affect gene expression and its regulation.
Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.
The central complex, a highly conserved insect brain region important for navigation, is characterized by a high degree of recurrence and a sparseness of output pathways.
The collective action of six transcription factors selects and activates the regulatory regions of the HSN serotonergic neuron effector genes constituting a signature that can be used for the novo identification of HSN expressed genes.
An innate immune system signaling pathway in planarians has a dual role: it enhances apoptosis during bacterial infection, but represses apoptosis during tissue regeneration in the absence of infection.
A computational strategy for extracting representative numerical features from 3D microscopy data enables in-depth quantitative analysis of cell and tissue organization through machine learning-driven data integration and context-guided visualization.
Quantitative analyses of multi-dimensional microscopy datasets with a new Fiji/ImageJ plugin for cell tracking reveal the lineage restrictions and morphogenetic cellular behaviors underlying embryonic limb outgrowth in the direct developing crustacean Parhyale hawaiensis.
