Model identification of neural encoding is an accessible system for the analysis of neural data that allows identifying and characterizing arbitrary relationships between neural activity and task-related variables such as behavior, stimuli, or internal states.

In three different regions including the United States, China, and Africa, virus discoveries were strongly associated with GDP and land use, but were less likely to be associated with climate and biodiversity variables than at a global scale.

A previously unknown lipid modification by a bacterial antibiotic resistance enzyme was identified through the analysis of a family of antibiotic resistance enzyme sequences with a machine learning approach.

Bioinformatic analysis of gene expression in chimeric heterokaryons can identify novel regulators of cell lineage.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

FlyBrainLab is an open-source computing platform that integrates 3D exploration and visualization of diverse Drosophila connectomic/synaptomic datasets with interactive exploration of the functional logic of modeled executable brain circuits.

The integration of multiple omics panels obtained from bone tissue could aid in the evaluation of the postmortem interval in forensic investigations, expanding the boundaries of forensic research.

A novel mouse model which permits tracking of papillomavirus infected cells in mouse stratified epithelia during early infection by means of genetic reporter activation.

A combination of massively parallel reporter assays and mass spectrometry uncovers the regulation of previously unexplored promoters across the Escherichia coli genome.

Quantification of uncertainty in drug dose-responses provides a more robust metric for differentiating drugs and identifying biomarkers of response.