Results 221 to 230 of about 6,810,610 (247)

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual Graph Contrastive Learning. [PDF]

Adv Sci (Weinh)
Yu Z, Yang Y, Chen X, Wong KC, Zhang Z, Zhao Y, Li X.   +6 more
europepmc   +1 more source

Polydopamine Nanoparticle‐Mediated Precise Near‐Infrared Optical Stimulation for Cognitive Enhancement

Advanced Science, EarlyView.
Polydopamine nanoparticles enable a precise, non‐genetic, and transcranial neuromodulation strategy via near‐infrared photothermal stimulation. By activating TRPV1 channels, this approach specifically enhances hippocampal gamma oscillations, thereby rescuing spatial memory deficits in models of perioperative neurocognitive disorder.
Yan‐Bo Zhou, Qiong Xue, Su‐Xuan Hou, Ke‐Yao Zhang, Wei‐Tong Pan, Ting‐Ting Zeng, Yu‐Ge Wang, Daqing Ma, Chenguang Zhao, Pan‐Miao Liu, Jian‐Jun Yang   +10 more
wiley   +1 more source

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks. [PDF]

Brief Bioinform
Si Y, Huang Z, Fang Z, Yuan Z, Huang Z, Li Y, Wei Y, Wu F, Yao YF.   +8 more
europepmc   +1 more source

Graph contrastive learning of subcellular-resolution spatial transcriptomics improves cell type annotation and reveals critical molecular pathways. [PDF]

Brief Bioinform
Lu Q, Ding J, Li L, Chang Y.
europepmc   +1 more source

Hierarchical graph contrastive learning of local and global presentation for multimodal sentiment analysis. [PDF]

Sci Rep
Du J, Jin J, Zhuang J, Zhang C.
europepmc   +1 more source

A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data. [PDF]

Brief Bioinform
Zhang L, Liang S, Wan L.
europepmc   +1 more source

MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction. [PDF]

Molecules
Hu B, Yu Z, Li M.
europepmc   +1 more source

ArieL: Adversarial Graph Contrastive Learning

ACM Transactions on Knowledge Discovery From Data
Contrastive learning is an effective unsupervised method in graph representation learning, and the key component of contrastive learning lies in the construction of positive and negative samples. Previous methods usually utilize the proximity of nodes in
Yada Zhu, Baoyu Jing, Hanghang Tong
exaly   +1 more source

Unifying Graph Contrastive Learning via Graph Message Augmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph contrastive learning is usually performed by first conducting Graph Data Augmentation (GDA) and then employing a contrastive learning pipeline to train GNNs. As we know that GDA is an important issue for graph contrastive learning.
Jin Tang, Bin Luo, Bo Jiang
exaly   +1 more source

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Cross-view graph contrastive learning with hypergraph

Information Fusion, 2023
Weixin Zeng
exaly