AIMC Topic: Cell Communication

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scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Briefings in bioinformatics
Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive g...

Deep Learning-Based 3D Single-Cell Imaging Analysis Pipeline Enables Quantification of Cell-Cell Interaction Dynamics in the Tumor Microenvironment.

Cancer research
UNLABELLED: The three-dimensional (3D) tumor microenvironment (TME) comprises multiple interacting cell types that critically impact tumor pathology and therapeutic response. Efficient 3D imaging assays and analysis tools could facilitate profiling a...

MACC: a visual interactive knowledgebase of metabolite-associated cell communications.

Nucleic acids research
Metabolite-associated cell communications play critical roles in maintaining the normal biological function of human through coordinating cells, organs and physiological systems. Though substantial information of MACCs has been continuously reported,...

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning.

Current Alzheimer research
BACKGROUND: Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation ...

Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning.

Briefings in bioinformatics
Although some pyroptosis-related (PR) prognostic models for cancers have been reported, pyroptosis-based features have not been fully discovered at the single-cell level in hepatocellular carcinoma (HCC). In this study, by deeply integrating single-c...

Graph deep learning enabled spatial domains identification for spatial transcriptomics.

Briefings in bioinformatics
Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and ce...

RAPPPID: towards generalizable protein interaction prediction with AWD-LSTM twin networks.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods for the prediction of protein-protein interactions (PPIs), while important tools for researchers, are plagued by challenges in generalizing to unseen proteins. Datasets used for modelling protein-protein predictions ...

A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of ...

LR-GNN: a graph neural network based on link representation for predicting molecular associations.

Briefings in bioinformatics
In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discov...

DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. A...