AI Medical Compendium Journal:
Communications biology

Showing 51 to 60 of 154 articles

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Communications biology
Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. Howev...

Revealing the mechanisms of semantic satiation with deep learning models.

Communications biology
The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these me...

VespAI: a deep learning-based system for the detection of invasive hornets.

Communications biology
The invasive hornet Vespa velutina nigrithorax is a rapidly proliferating threat to pollinators in Europe and East Asia. To effectively limit its spread, colonies must be detected and destroyed early in the invasion curve, however the current relianc...

A neural network-based model framework for cell-fate decisions and development.

Communications biology
Gene regulatory networks (GRNs) fulfill the essential function of maintaining the stability of cellular differentiation states by sustaining lineage-specific gene expression, while driving the progression of development. However, accounting for the r...

OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion.

Communications biology
Epithelial ion and fluid transport studies in patient-derived organoids (PDOs) are increasingly being used for preclinical studies, drug development and precision medicine applications. Epithelial fluid transport properties in PDOs can be measured th...

Building trust in deep learning-based immune response predictors with interpretable explanations.

Communications biology
The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I m...

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Communications biology
Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domai...

Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli.

Communications biology
The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and anti...

Skeletal muscle cells opto-stimulation by intramembrane molecular transducers.

Communications biology
Optical stimulation and control of muscle cell contraction opens up a number of interesting applications in hybrid robotic and medicine. Here we show that recently designed molecular phototransducer can be used to stimulate C2C12 skeletal muscle cell...

Deep learning-based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra.

Communications biology
Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quantify cell numbers, and fluorescence signals within cellular compartments, derived from RNAscope or immunohistochemistry. We utilised DLAP to analyse su...