Results 41 to 50 of about 106,924 (261)
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
core +1 more source
Attention-driven Graph Clustering Network
Proceedings of the 29th ACM International Conference on Multimedia, 2021 The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.
Zhihao Peng 0002 +3 more
openaire +2 more sources
Molecular Oncology, EarlyView.We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric +10 more
wiley +1 more source
Molecular Oncology, EarlyView.Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova +14 more
wiley +1 more source
CCDC80 suppresses high‐grade serous ovarian cancer migration via negative regulation of B7‐H3
Molecular Oncology, EarlyView.PAX8 is a lineage‐specific master regulator of transcription in high‐grade serous ovarian cancer (HGSC) progression. We show for the first time that PAX8 facilitates proliferation and metastasis by repressing the cell autonomous tumor suppressor CCDC80 and inducing B7‐H3 expression.
Aya Saleh +12 more
wiley +1 more source
Predicting Protein–Protein Interactions via Gated Graph Attention Signed Network
, 2021 Protein–protein interactions (PPIs) play a key role in signal transduction and pharmacogenomics, and hence, accurate PPI prediction is crucial. Graph structures have received increasing attention owing to their outstanding performance in machine learning.
Zhijie Xiang +5 more
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E2A selectively regulates TGF‐β–induced apoptosis in KRAS‐mutant non‐small cell lung cancer
Molecular Oncology, EarlyView.Ability to induce apoptosis by TGF‐β is frequently lost in advanced lung adenocarcinoma despite intact TGF‐β signaling. We identify E2A as a mutant KRAS–dependent mediator of resistance to TGF‐β–induced apoptosis. TGF‐β induces E2A via SMAD3 in mutant KRAS cells, and E2A silencing restores apoptosis and enhances radiation response in cell lines ...
Sergei Chuikov +3 more
wiley +1 more source
, 2023 Protein design energy functions have been developed over decades by leveraging physical forces approximation and knowledge-derived features. However, manual feature engineering and parameter tuning might suffer from knowledge bias.
Liu, D +4 more
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Molecular Oncology, EarlyView.This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source
Molecular Oncology, EarlyView.Combining osimertinib with the STING agonist ADU‐S100 activates innate and adaptive immunity to overcome the non‐inflamed microenvironment of Egfr‐mutant lung cancer. This combination increases NK and CD8+ T‐cell infiltration, associated with activation of the STING‐IRF3 pathway and local immunogenic cell death.
Jun Nishimura +19 more
wiley +1 more source