Results 181 to 190 of about 30,503 (277)

IGCLAPS: an interpretable graph contrastive learning method with adaptive positive sampling for scRNA-seq data analysis. [PDF]

Bioinformatics
Zheng W, Min W, Wang S.
europepmc   +1 more source

WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing. [PDF]

Micromachines (Basel), 2023
Liu R, Wang X, Kumar A, Sun B, Zhou Y.
europepmc   +1 more source

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen, Renato S. M. Almeida, Stefan Flauder, Guillaume Couégnat, Kamen Tushtev, Jürgen Horvath, Peter Wriggers, Kurosch Rezwan   +7 more
wiley   +1 more source

Relational similarity-based graph contrastive learning for DTI prediction. [PDF]

Brief Bioinform
Bian J, Lu H, Wei L, Li Y, Wang G.
europepmc   +1 more source

scGCL: an imputation method for scRNA-seq data based on graph contrastive learning. [PDF]

Bioinformatics, 2023
Xiong Z, Luo J, Shi W, Liu Y, Xu Z, Wang B.   +5 more
europepmc   +1 more source

Unveil Fundamental Graph Properties for Neural Architecture Search

Advanced Science, EarlyView.
This paper proposes NASGraph, a graph‐based framework that represents neural architectures as graphs whose structural properties determine performance. By revealing structure–performance relationships, NASGraph enables efficient neural architecture search with significantly reduced computation.
Zhenhan Huang, Tejaswini Pedapati, Pin‐Yu Chen, Chunheng Jiang, Jianxi Gao   +4 more
wiley   +1 more source

SpaICL: image-guided curriculum strategy-based graph contrastive learning for spatial transcriptomics clustering. [PDF]

Brief Bioinform
Zhao J, Min W.
europepmc   +1 more source

3D graph contrastive learning for molecular property prediction. [PDF]

Bioinformatics, 2022
Moon K, Im HJ, Kwon S.
europepmc   +1 more source

Deep Learning‐Powered Scalable Cancer Organ Chip for Cancer Precision Medicine

Advanced Science, EarlyView.
This scalable, low‐cost Organ Chip platform, made via injection molding, uses capillary pinning for hydrogel confinement and supports versatile tissue coculture and robust imaging. Deep learning enables label‐free, sensitive phenotypic analysis.
Yu‐Chieh Yuan, Beibei Xu, Jenna McCormack, XuHai Huang, Jingzhe Ma, Thomas Marshall, Yacong Sun, Hardeep Singh, Alyssa Fanelli, Gauri Kulkarni, Ji Hye Seo, Paige Gilbride, Bing Wei, Bo Wang, Yanyan Liu, Fei Ma, Lin Zhou, Shuyang Wang, Xiaohua Qian, Zhiyong Xie, Polina Golland, Longlong Si, Yu Shrike Zhang, Xin Xie, Haiqing Bai   +24 more
wiley   +1 more source

CLMT: graph contrastive learning model for microbe-drug associations prediction with transformer. [PDF]

Front Genet
Xiao L, Wu J, Fan L, Wang L, Zhu X.
europepmc   +1 more source