Browse our latest Computational and Systems Biology articles

Structural differences in adolescent brains associated with binge drinking might be preceding the onset of such behavior, suggesting a reevaluation of studies of the effects of alcohol on the adolescent brain.

A genome-wide map of human allele-specific methylation using long-read sequencing detects novel imprinted DNA methylation events and reveals large blocks of subtle parent-of-origin bias in DNA methylation with mutual exclusive allelic H3K36me3 and H3K27me3 histone modifications.

A new computational approach for detecting sleep waveforms reveals that the 11–15 Hz sleep 'spindle', a neural rhythm implicated in memory consolidation, co-occurs widely across cortex much more often than previously thought.

Robotic AI system automated discovery of optimum cell culture protocol, a most experience-dependent process in regenerative medicine.

A novel model is presented that reconciles in-cell structure probing data with splicing regulatory elements to predict exon 10 inclusion of the Tau gene with high accuracy for 53 splice altering mutations.

The integration of experimental and data mining approaches provides novel insights into deubiquitinating enzymes individually and as a gene family.

Recurrent neural network models enable prediction and design of health-relevant metabolite dynamics in synthetic human gut communities.

Applying computational modeling to quantify threat learning processes uncovers how variations in these conserved learning processes relate to anxiety severity and the neuroanatomical substrates moderating these associations.

The discovery of uncharacterized lncRNAs that regulate cell proliferation/growth across cancer types, including two p53 regulated tumor suppressive lncRNAs, were identified through systematic analyses of multi-omics data and provides a computational framework resource to cancer researchers.

Method NEAdriver employs knowledge from global networks to predict novel cancer driver genes in an individualized manner, which is done by accounting for mutations’ co-occurrence in each tumor genome and rigorous statistical evaluation.