Browse our latest Computational and Systems Biology articles

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.

Osteogenic growth peptide displays agonistic activity at CB2.

Global proteomic and acetylomic analyses reveal how skeletal muscle adapts to high-intensity interval training including adaptations to processes regulating metabolism and contraction.

Advanced machine learning based brain signal decoding of grip-force as a proxy for movement vigor shows Parkinson's disease related performance reduction, suggestive of a loss of cortical vigor encoding in the absence of dopamine.

Coordinated regulation of co-complex members compensates for aneuploidy-induced stoichiometric imbalances in human cancer.

The LOTUS initiative builds, through the Wikidata knowledge graph, a virtuous cycle of data sharing practices for natural products research.

aLENS implements new parallel methods to resolve cytoskeletal assembly dynamics with high stability, physical consistency, and scalability.

A newly developed computational framework provides an overview of the extent, diversity, and relevance of the genes of unknown function in genomes and metagenomes.

The generalizability of CODOP in distinct world regions and its flexibility to reckon with the changing availability of hospital resources makes it a clinically useful tool potentially improving the outcome prediction and the management of COVID-19 hospitalized patients.