Results 121 to 130 of about 8,049,750 (250)

Convolutional Models with Multi-Feature Fusion for Effective Link Prediction in Knowledge Graph Embedding. [PDF]

Entropy (Basel), 2023
Guo Q, Liao Y, Li Z, Lin H, Liang S.
europepmc   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining. [PDF]

BMC Bioinformatics, 2023
Djeddi WE, Hermi K, Ben Yahia S, Diallo G.   +3 more
europepmc   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes. [PDF]

PLoS Comput Biol, 2023
Wu YH, Huang YA, Li JQ, You ZH, Hu PW, Hu L, Leung VCM, Du ZH.   +7 more
europepmc   +1 more source

Nonlinear Dynamic Field Embedding: On Hyperspectral Scene Visualization [PDF]

, 2012
In many areas of research, complex signals are commonly represented by high dimensional feature vectors. However, high dimensional vectors are difficult to analyze and interpret due to the curse of dimensionality.
Lunga, Dalton, Erosy, Okan
core  

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. [PDF]

PeerJ, 2023
Salcedo MV, Gravel N, Keshavarzi A, Huang LC, Kochut KJ, Kannan N.   +5 more
europepmc   +1 more source

Klotho‐Derived Peptide 1 Protects against Acute Kidney Injury by Directly Targeting Mitochondrial ATAD3A

Advanced Science, EarlyView.
This study uncovers that KP1, a Klotho‐derived peptide, protects the kidney against acute kidney injury, a clinical syndrome with high morbidity and mortality. KP1 enters tubular epithelial cells via endocytosis, directly interacts with the mitochondrial ATAD3A/HIGD2A complex, thereby preventing cytochrome c release, and caspases activation, and ...
Xiaoyao Zhang, Shihui Lin, Tianyu Wu, Zhixin Zhang, Hong Zhou, Xue Hong, Youhua Liu   +6 more
wiley   +1 more source

Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model. [PDF]

Evid Based Complement Alternat Med, 2023
Ye Q, Yang R, Cheng CL, Peng L, Lan Y.
europepmc   +1 more source