Results 201 to 210 of about 142,397 (310)

A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network. [PDF]

Comput Intell Neurosci, 2022
Zhu L, Pan X, Wang X, Haito F.
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network. [PDF]

BMC Bioinformatics
Wei PJ, Zhou J, Cao RF, Ding Y, Yue Z, Zheng CH.   +5 more
europepmc   +1 more source

Spatial Attention-Based 3D Graph Convolutional Neural Network for Sign Language Recognition. [PDF]

Sensors (Basel), 2022
Al-Hammadi M, Bencherif MA, Alsulaiman M, Muhammad G, Mekhtiche MA, Abdul W, Alohali YA, Alrayes TS, Mathkour H, Faisal M, Algabri M, Altaheri H, Alfakih T, Ghaleb H.   +13 more
europepmc   +1 more source

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

Advanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser, Haseeb Muhammad, Junaid Younas, Gillian Lovell, Bianca Migliori, Andreas Dengel, Sheraz Ahmed   +6 more
wiley   +1 more source

Attention aware edge-node exchange graph neural network

Dianxin kexue
An attention aware edge-node exchange graph neural network (AENN) model was proposed, which used edge-node switched convolutional graph neural network method for graph encoding in a graph structured data representation framework for semi supervised ...
Ruiqin WANG, Yimin HUANG, Qishun JI, Chaoyi WAN, Zhifeng ZHOU   +4 more
doaj   +2 more sources

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants. [PDF]

J Chem Inf Model
Monsia R, Gundry SC, Mohr ML, Smith MA, Bhattacharyya S, Hati S.   +5 more
europepmc   +1 more source

A New Regression Model for Depression Severity Prediction Based on Correlation among Audio Features Using a Graph Convolutional Neural Network. [PDF]

Diagnostics (Basel), 2023
Ishimaru M, Okada Y, Uchiyama R, Horiguchi R, Toyoshima I.   +4 more
europepmc   +1 more source

Majority‐Voting Overlapping Method for Error Correction in DNA Data Storage

Advanced Intelligent Discovery, EarlyView.
We propose an overlapping‐based majority‐voting method for DNA data storage error correction. By aligning multiple reads and choosing the most frequent base per position, it suppresses substitution errors without prior models. Validated on synthetic and real sequencing data, it achieves high‐fidelity, scalable, and cost‐effective reconstruction ...
Thi Bich Ngoc Nguyen, Seongjun Seo, Anshula Tandon, Nguyen Kim Uyen, Thi Hong Nhung Vu, Sung Ha Park   +5 more
wiley   +1 more source

Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network. [PDF]

Sci Rep
Yin G, Yuan J, Chen Y, Guo G, Shi D, Wang L, Zhao Z, Zhao Y, Zhang M, Dong Y, Wang B, Tan S.   +11 more
europepmc   +1 more source