Browse our latest Computational and Systems Biology articles

Classical theoretical approaches to studying the cerebellar cortex are generalized to a broader set of learning tasks, revealing that the optimal value for the sparsity of granule cell activity is task-dependent.

A novel integrative biological and psychological mechanistic explanation links cognitive processes and neurotransmitters across human development and establishes their involvement in intelligent behavior.

Dimensions for reinforcement learning reduced by dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional components.

An interpretable machine learning approach can recapitulate the molecular features associated to immune protein-protein binding and leverage them for the prediction of immune response specificity.

High proportions of gut bacteria that produce their own food can be an indicator for poor gut health.

A near-linear relationship between electrical stimulus duration and resultant joint torque in stick insects has been unveiled, enhancing control strategies in biohybrid robotics.

GENIUS provides a deep learning framework for multi-omics integration that transforms data into spatial images, using integrated gradients to reveal novel and critical cancer gene association.

Heterogeneity in the population of granule cells in the dentate gyrus, which includes immature and mature neurons with distinct intrinsic properties, contributes to the population's enhanced encoding capabilities and ability to discriminate correlated stimuli.

Deciphering the dynamics of the allosteric communication and the formation of transient pockets along the activation pathway of G-protein-coupled receptors provides useful information for the design of allosteric modulators.