Results 161 to 170 of about 149,463 (272)

STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data. [PDF]

Bioinformatics
Wang C, Yu X.
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

Cauchy Graph Convolutional Networks

International Journal of Approximate Reasoning
Taurai Muvunza, Yang Li 0104, Ercan Engin Kuruoglu   +2 more
openaire   +1 more source

Graph convolutional networks

, 2022
Heidari, Negar, Hedegaard Morsing, Lukas, Iosifidis, Alexandros   +2 more
openaire   +2 more sources

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

Innovative application of a traffic-prediction spatio-temporal graph convolutional network for dengue disease forecasting. [PDF]

Sci Rep
Siabi N, Son R, Thomas M, Irrgang C, Saynisch-Wagner J.   +4 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

OptiSyn: an interpretable, multi-omics-driven graph convolutional network framework for synergy-oriented drug combination design in disease treatment. [PDF]

Chin Med
Shi Y, Liu J, Zeng G, Wang Y, Guan S, Li M, Wang S, Yu Y, Yang W, Wang Z.   +9 more
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition. [PDF]

Front Neurosci
Han Y, Chen Y, Ruan H, Song D, Xu H, Zhu H.   +5 more
europepmc   +1 more source