Results 81 to 90 of about 4,082,283 (306)
Topological Properties of Neuromorphic Nanowire Networks
Frontiers in Neuroscience, 2020 Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and
Alon Loeffler +8 more
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Graph neural networks for materials science and chemistry
Communications Materials, 2022 Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and ...
Patrick Reiser +10 more
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Analyzing the Sensitivity of Deep Neural Networks for Sentiment Analysis: A Scoring Approach
, 2020 Part of IEEE WCCI 2020 is the world’s largest technical event on computational intelligence, featuring the three flagship conferences of the IEEE Computational Intelligence Society (CIS) under one roof: The 2020 International Joint Conference on Neural ...
Wei Emma Zhang +7 more
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Spatial biology in cancer epigenetics
Molecular Oncology, EarlyView.Spatial epigenomics combines molecular profiling with tissue architecture to reveal how gene regulation is organized within intact tissues. In cancer, these technologies uncover the mechanisms driving tumor heterogeneity and microenvironmental interactions, opening new opportunities for biomarker discovery and precision medicine.
Eva Crespo‐García, Manel Esteller
wiley +1 more source
MGATs: Motif-Based Graph Attention Networks
MathematicsIn recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks.
Jinfang Sheng +3 more
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Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud
Sensors, 2022 Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation.
Tae-Won Jung +5 more
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A Tropical View of Graph Neural Networks
, 2023 Learning dynamic programming algorithms with Graph Neural Networks (GNNs) is a research direction which is increasingly gaining popularity. Prior work has demonstrated that in order to learn such algorithms, it is necessary to have an ``alignment ...
Bacciu, Davide +2 more
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ADP‐ribosylation: An emerging regulator of the epigenome
Molecular Oncology, EarlyView.ADP‐ribosylation has emerged as a dynamic epigenetic signaling mechanism that modifies histones and chromatin‐associated proteins. Through coordinated PARylation and MARylation, it integrates with other histone modifications to regulate chromatin structure, transcription factor activity, and gene expression, influencing genome function and disease ...
Cristel V. Camacho +2 more
wiley +1 more source
A Survey on Graph Neural Networks
IEEE AccessGraph Neural Networks (GNNs) have emerged as a fundamental class of models for analyzing graph-structured data, with broad applications spanning social networks, computational neuroscience, and intelligent transportation systems. In contrast to Euclidean data, graphs pose distinctive challenges due to their irregular topology, permutation invariance ...
Xinyang Zhang +8 more
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Entropy, 2023 Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes.
Ying Xi, Xiaohui Cui
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