scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac670
Abstract: MOTIVATION The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic,… read more here.
Keywords: cell; pseudotime; schicptr unsupervised; graph refinement ... See more keywords
Joint Adaptive Dual Graph and Feature Selection for Domain Adaptation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2021.3073937
Abstract: Domain adaptation aims to exploit domain-invariant features by aligning the cross-domain distributions in the manifold subspace for applying the classifier trained on the source domain to the target domain. However, two limitations may still deteriorate… read more here.
Keywords: domain; dual graph; domain adaptation; feature selection ... See more keywords
Robust Dual Graph Self-Representation for Unsupervised Hyperspectral Band Selection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3203207
Abstract: Unsupervised band selection aims to select informative spectral bands to preprocess hyperspectral images (HSIs) without using labels. Traditional band selection methods only work well on Euclidean data, but ignore structural information of pixels and spectral… read more here.
Keywords: band; dual graph; band selection; robust dual ... See more keywords
Few-Shot Learning for Fault Diagnosis With a Dual Graph Neural Network
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2022.3205373
Abstract: Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Deep learning-based methods have been recently developed for fault diagnosis due to their advantages in feature representation. However, most… read more here.
Keywords: fault diagnosis; diagnosis; graph neural; shot learning ... See more keywords
Dual-Graph Global and Local Concept Factorization for Data Clustering.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3177433
Abstract: Considering a wide range of applications of nonnegative matrix factorization (NMF), many NMF and their variants have been developed. Since previous NMF methods cannot fully describe complex inner global and local manifold structures of the… read more here.
Keywords: concept factorization; local concept; graph global; factorization ... See more keywords
Few-Shot Relation Extraction With Dual Graph Neural Network Interaction.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3278938
Abstract: Recent advances in relation extraction with deep neural architectures have achieved excellent performance. However, current models still suffer from two main drawbacks: 1) they require enormous volumes of training data to avoid model overfitting and… read more here.
Keywords: dual graph; graph; shot relation; relation extraction ... See more keywords
Mesh Neural Networks Based on Dual Graph Pyramids.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on visualization and computer graphics"
DOI: 10.1109/tvcg.2023.3257035
Abstract: Deep neural networks (DNNs) have been widely used for mesh processing in recent years. However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most DNNs expect 2-manifold, watertight meshes, but many… read more here.
Keywords: neural networks; mesh neural; based dual; graph pyramids ... See more keywords
Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
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DOI: 10.1155/2017/1096028
Abstract: Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent… read more here.
Keywords: rank representation; latent low; low rank; dual graph ... See more keywords
RNA-As-Graphs Motif Atlas—Dual Graph Library of RNA Modules and Viral Frameshifting-Element Applications
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Molecular Sciences"
DOI: 10.3390/ijms23169249
Abstract: RNA motif classification is important for understanding structure/function connections and building phylogenetic relationships. Using our coarse-grained RNA-As-Graphs (RAG) representations, we identify recurrent dual graph motifs in experimentally solved RNA structures based on an improved search… read more here.
Keywords: viral frameshifting; rna; rna modules; dual graph ... See more keywords