Results 221 to 230 of about 142,397 (310)

Attention mechanism-enhanced graph convolutional neural network for unbalanced lithology identification. [PDF]

Sci Rep
Wang A, Zhao S, Xie K, Wen C, Tian HL, He JB, Zhang W.   +6 more
europepmc   +1 more source

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. [PDF]

Neuroimage, 2022
Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y.   +5 more
europepmc   +1 more source

Extending convolutional neural networks to irregular domains through graph inference

, 2017
Bastien Pasdeloup
openalex   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network. [PDF]

Front Comput Neurosci, 2022
Saikumar K, Rajesh V, Srivastava G, Lin JC.   +3 more
europepmc   +1 more source

Graph Convolutional Neural Network-based Modeling of Ultra-Scale MIMO Channels in 6G Networks [PDF]

Jinhui Chen, Haochen He, Zhan Xu
openalex   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism. [PDF]

Front Comput Neurosci
Chen W, Liao Y, Dai R, Dong Y, Huang L.
europepmc   +1 more source

Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes. [PDF]

Sci Rep, 2022
Asadzadeh S, Yousefi Rezaii T, Beheshti S, Meshgini S.   +3 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source