Browse our latest Computational and Systems Biology articles

Unbiased and automatic annotation using structured prediction framework with efficiently built data-driven atlases is more accurate than registration-based methods for cell identifications in dense images and enables fast whole-brain analysis.

'Cellular Electron Microscopy 500,000 images' (CEM500K) is a highly heterogeneous, information-rich, non-redundant, unlabeled EM dataset curated to pre-train DL algorithms for better model generalization on EM segmentation tasks.

A comparison of different computational models reveals that looking behavior, during value-based choices from 9, 16, 25, or 36 snack foods, actively influences the subjective values of the available alternatives.

A human experimental model for physiological glucocorticoid exposure and glucocorticoid withdrawal identifies a multi-omic cluster, including microRNA miR-122-5p and metabolites, associated with glucocorticoid-responsive genes.

Elucidating the control and regulation of acetate overflow in E. coli reveals that acetate is not only an end-product but also a co-substrate and a global regulator of glucose metabolism.

A single-cell atlas of the developing Drosophila wing disc at two time points enables a detailed understanding of the signals exchanged between different cell populations during development.

Global gene expression control mechanisms are modeled using a large dataset consisting of absolute-quantitative proteomics, transcriptomics, and partial metabolomics of Saccharomyces cerevisiae at a wide range of growth conditions.

Network topology and gene expression patterns in small cell lung cancer reveal two mutually opposing teams of regulators that enable multistability and consequent non-genetic heterogeneity, a clinically unsolved challenge.

The fact that sexual activity/reproduction doubles the lifespan of certain rodent species is most likely linked to critical changes in the regulation of the hypothalamic–pituitary–adrenal stress axis.

A deep learning-based pipeline was developed for extracting cellular signals flexibly from moving cells in 3D time lapse images, and it outperformed previous methods under different imaging conditions.