GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions. [PDF]
Front Genet, 2022 He J +5 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo +5 more
wiley +1 more source
3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network. [PDF]
Brief BioinformTang C +7 more
europepmc +1 more source
Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network. [PDF]
Brain Sci, 2022 Meng L, Ge K.
europepmc +1 more source
Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents
Advanced Intelligent Discovery, EarlyView.This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer +3 more
wiley +1 more source
Superpixel-based graph convolutional neural network for polarimetric synthetic aperture radar image classification. [PDF]
Sci RepImani M.
europepmc +1 more source
LABAMPsGCN: A framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network. [PDF]
Front Genet, 2022 Sun TJ +5 more
europepmc +1 more source
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
THGC_MDA: a method for predicting the associations between m<sup>1</sup>A modification and diseases based on ternary heterogeneous network and graph convolutional neural network. [PDF]
Front GenetGao H, Zhou X, Bai L, Yang H, Liu F.
europepmc +1 more source