Results 261 to 270 of about 84,208 (297)

A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest. [PDF]

BMC Bioinformatics
Kuang H, Zhang Z, Zeng B, Liu X, Zuo H, Xu X, Wang L.   +6 more
europepmc   +1 more source

Disorder‐Induced Extremely Low Thermal Conductivity of Graphite Fluoride

Advanced Science, EarlyView.
This article investigates the effect of fluorination on the thermal conductivity of graphite fluoride, demonstrating the relationship between structural modification and thermal conductivity in an extreme case. The record‐low through‐plane thermal conductivity measured in exfoliated graphite fluoride flakes was found to correlate with the wide ...
Wonsik Lee, Donghoon Moon, Ziyan Qian, Jinwoo Kim, Changheon Kim, Yeojin Lee, Hyobin Yoo, Richard B. Wilson, Qiye Zheng, Gwan‐Hyoung Lee, Hyejin Jang   +10 more
wiley   +1 more source

Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance. [PDF]

Cureus
Harris J, Yadalam PK, Anegundi RV, Arumuganainar D.   +3 more
europepmc   +1 more source

Activation of ERBB4 Pathway Inhibits Pathological Transdifferentiation of Lung Epithelial Progenitors into CD66c+ Basal Cells in Severe Lung Injury

Advanced Science, EarlyView.
In fibrotic distal lung regions, CD66c+ basal cells emerge as a pathological state. Using human distal lung organoids, this study identifies CD66c+ basal cells as a pro‐fibrotic state arising through transdifferentiation from secretory, AT2, and basal cells.
Kaijun Lin, Xinran Deng, Haonan Wang, Yamei Jiang, Jian Sun, Hailin Ding, Ming Ye, Xiaoting Wang, Yu Wang, Li Yuan, Zhenju Song, Xinhua Lin, Shenfei Sun, Ning Jiang   +13 more
wiley   +1 more source

Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks. [PDF]

Brief Bioinform
Baul S, Tanvir Ahmed K, Jiang Q, Wang G, Li Q, Yong J, Zhang W.   +6 more
europepmc   +1 more source

Icariin Enhances the Enzymatic Activity of N‐acetylgalactosaminidase to Augment Akkermansia Abundance in Gut Microbiota for Improved PD‐1 Blockade Efficacy in Tumor Suppression

Advanced Science, EarlyView.
Icariin promoted the growth of Akk by enhancing the activity of N‐acetylgalactosaminidase (Amuc_0920), which enhanced mucin utilization and provided a favorable nutrient environment for bacterial growth. This icariin‐mediated enrichment of Akk further reshaped the tumor microenvironment and promoted CD8+ T cell infiltration, ultimately synergizing with
Shuangying Qiao, Liu Yang, Haibang Hao, Qiuxia Ding, Feng Ding, Zheng Chen, Jinfang Zhang, Yun He, Meng Li, Jun Xu, Chao Wang, Aiping Lu, Fangfei Li   +12 more
wiley   +1 more source

Tumor‐Derived Alpha‐1 Antitrypsin Promotes Liver Metastasis of Colorectal Cancer Through the Neutrophil Extracellular Traps–CCDC25 Pathway

Advanced Science, EarlyView.
ABSTRACT Liver metastasis is a leading cause of mortality in colorectal cancer (CRC), where the inflammatory tumor microenvironment, specifically neutrophil infiltration, significantly promotes metastatic colonization. This study reveals a pro‐metastatic role for alpha‐1 antitrypsin (A1AT) in CRC liver metastasis via a dual mechanism involving ...
Qian Fei, Chunning Li, Lei Zhan, Xiaoyan Li, Yan Hui, Yue Jin, Jing Zhang, Lu Yang, Xin Sun, Xiaoxi Li, Qian Dong, Jingdong Zhang   +11 more
wiley   +1 more source

Simple and deep graph attention networks [PDF]

Knowledge-Based Systems
Graph Attention Networks (GATs) and Graph Convolutional Neural Networks (GCNs) are two state-of-the-art architectures in Graph Neural Networks (GNNs). It is well known that both models suffer from performance degradation when more GNN layers are stacked,
Hanchen Wang, Xuemin Lin
exaly   +3 more sources

Graph neural networks with multiple kernel ensemble attention

Knowledge-Based Systems, 2021
Graph neural networks have recently attracted increasing research attention. Recent research has shown that applying the attention mechanism is helpful for graph learning. The performance of the attention function is critical for graph learning.
Haimin Zhang, Min Xu
exaly   +2 more sources

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Structural attention network for graph

Applied Intelligence, 2021
We present a structural attention network (SAN) for graph modeling, which is a novel approach to learn node representations based on graph attention networks (GATs), with the introduction of two improvements specially designed for graph-structured data. The transition matrix was used to differentiate the structures between the nodes.
Anzhong Zhou, Yifen Li
openaire   +1 more source