Leave-One-Trial-Out (LOTO) is a general, efficient and easily implementable approach for inferring trial-by-trial measures of computational model parameters in order to link these measures to neural mechanisms.

Sparse patterns in gene expression allow simultaneous discovery of cell states, lineage relationships and key genes controlling developmental decisions.

A combined experimental and modeling approach provides insight into potential biases when inferring transcription rates from static mRNA distributions, and shows that correcting for cell-cycle phase and post-transcriptional noise provides rates that agree with live-cell transcription measurements.

Targeted therapies induce an aberrant fucosylation of complex tumor secretomes stimulating the expansion of minority drug-resistant clones and promoting therapy resistance.

The integration of multi-nucleosome configuration data with histone turnover and new chromatin accessibility data by systematically investigated 'regulated on-off-slide' models reveals promoter state transitions regulated by just one assembly process.

Endothelial Differentiation Factor 1 (EDF1) plays a critical role in driving mRNA-specific quality control and global transcriptional responses in response to ribosome collisions.