Results 71 to 80 of about 8,080,419 (296)
2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW)Graphs are useful for representing various realworld objects. However, graph neural networks (GNNs) tend to suffer from over-smoothing, where the representations of nodes of different classes become similar as the number of layers increases, leading to performance degradation.
Jun Kato 0008 +3 more
openaire +4 more sources
Exploiting Triangle Patterns for Heterogeneous Graph Attention Network
, 2022 Recently, graph neural networks (GNNs) have been improved under the influence of various deep learning techniques, such as attention, autoencoders, and recurrent networks.
Yi, Eunjeong, Kim, Min-Soo
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Molecular Oncology, EarlyView.The novel styrylquinazolinone‐based molecule W1B effectively suppresses glioblastoma by inhibiting IGF1R and EGFR. In high‐glucose microenvironments driving tumor resistance, W1B acts synergistically with the EGFR inhibitor dacomitinib. This combination safely blocks compensatory survival signaling in zebrafish xenograft models. Showcasing promising in
Patryk Rurka +9 more
wiley +1 more source
Multi-Order-Content-Based Adaptive Graph Attention Network for Graph Node Classification
, 2023 In graph-structured data, the node content contains rich information. Therefore, how to effectively utilize the content is crucial to improve the performance of graph convolutional networks (GCNs) on various analytical tasks. However, current GCNs do not
Xiao-Zhu Xie +3 more
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Molecular Oncology, EarlyView.Breast cancer remains a major cause of cancer death in women, frequently developing endocrine therapy resistance. This study demonstrates that upregulated p21‐activated kinase 1 (PAK1) activity drives resistance to tamoxifen and long‐term estrogen deprivation in ER+ breast cancer models.
Luisa Schwarzmüller +10 more
wiley +1 more source
Hybrid Graph Models for Traffic Prediction
Applied Sciences, 2023 Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions.
Renyi Chen, Huaxiong Yao
doaj +1 more source
Heterogeneous graph attention network
, 2020 A graph is a data structure which contains connection information in edges and feature information in nodes. Graph neural network applies deep learning to process graph and it becomes more and more popular by its powerful representation ability.
Wang, Zhixing
core
Molecular Oncology, EarlyView.Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur +11 more
wiley +1 more source
Aspect-based Sentiment Classification with Reinforced Dependency Graph
Taiyuan Ligong Daxue xuebao, 2022 We proposed Reinforced Dependency Graph for Aspect-based Sentiment Classification (RDGSC), a reinforced dependency graph model for aspect-based sentiment classification.
Hongyang SONG, Xiaofei ZHU, Jiafeng GUO
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Molecular Oncology, EarlyView.The proposed mechanism of action for the CDK12/13 inhibitor and cyclin K degrader, CT7439. CDK12/13 inhibition interrupts transcription elongation, leading to increased DNA damage that results in cell death. This agent is a potentially novel treatment option for patients with colorectal cancer. Created in BioRender. Cyclin‐dependent kinase (CDK) 12 and
Wylie K. Watlington +10 more
wiley +1 more source