Pre‐Training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
Sign Up to like & getrecommendations! Published in 2022 at "Advanced Science"
DOI: 10.1002/advs.202203796
Abstract: The latest biological findings observe that the motionless “lock‐and‐key” theory is not generally applicable and that changes in atomic sites and binding pose can provide important information for understanding drug binding. However, the computational expenditure… read more here.
Keywords: equivariant graph; graph matching; matching networks; drug binding ... See more keywords
Ranking docking poses by graph matching of protein–ligand interactions: lessons learned from the D3R Grand Challenge 2
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Computer-Aided Molecular Design"
DOI: 10.1007/s10822-017-0046-1
Abstract: A novel docking challenge has been set by the Drug Design Data Resource (D3R) in order to predict the pose and affinity ranking of a set of Farnesoid X receptor (FXR) agonists, prior to the… read more here.
Keywords: challenge; poses graph; method; graph matching ... See more keywords
A novel method for graph matching based on belief propagation
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.10.018
Abstract: Abstract Graph matching is a fundamental NP-problem in computer vision and pattern recognition. In this paper, we propose a robust approximate graph matching method. The match between two graphs is formulated as an optimization problem… read more here.
Keywords: graph; graph matching; novel method; belief propagation ... See more keywords
HOGMMNC: a higher order graph matching with multiple network constraints model for gene‐drug regulatory modules identification
Sign Up to like & getrecommendations! Published in 2019 at "Bioinformatics"
DOI: 10.1093/bioinformatics/bty662
Abstract: Motivation: The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene‐drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug… read more here.
Keywords: higher order; gene drug; drug; order graph ... See more keywords
Progressively Decomposing Graph Matching
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2908925
Abstract: Existing approaches to graph matching mainly include two types, i.e., the Koopmans-Beckmann’s QAP formulation (KB-QAP) and Lawler’s QAP formulation (L-QAP). The former is advantageous in scalability but disadvantageous in generality, while the latter is exactly… read more here.
Keywords: progressively decomposing; method; qap; graph ... See more keywords
Identify Multiple Gene-Drug Common Modules via Constrained Graph Matching
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3188503
Abstract: Identifying gene-drug interactions is vital to understanding biological mechanisms and achieving precise drug repurposing. High-throughput technologies produce a large amount of pharmacological and genomic data, providing an opportunity to explore the associations between oncogenic genes… read more here.
Keywords: drug common; common modules; gene drug; graph matching ... See more keywords
Deep Graph Matching Based Dense Correspondence Learning Between Non-Rigid Point Clouds
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3160237
Abstract: Building point-to-point dense correspondence between non-rigid shapes is a fundamental and challenging problem. Although functional map-based methods which calculate basis and convert point-wise map to functional map have shown promising performance on meshes, they are… read more here.
Keywords: non rigid; point clouds; graph matching; correspondence ... See more keywords
SemanticLoop: Loop Closure With 3D Semantic Graph Matching
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3229228
Abstract: Loop closure can effectively correct the accumulated error in robot localization, which plays a critical role in the long-term navigation of the robot. Traditional appearance-based methods rely on local features and are prone to failure… read more here.
Keywords: closure; graph matching; loop closure; object level ... See more keywords
Radio Resource Management for C-V2X Using Graph Matching and Actor–Critic Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2022.3213176
Abstract: We propose a hybrid centralized-distributed radio resource management (RRM) scheme for cellular vehicle-to-everything (C-V2X), which is to mitigate the interference caused by radio resource sharing between vehicle-to-infrastructure (V2I) links and vehicle-to-vehicle (V2V) links. Specifically, it… read more here.
Keywords: resource management; critic learning; resource; graph matching ... See more keywords
DeepSeed Local Graph Matching for Densely Packed Cells Tracking
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2019.2936851
Abstract: The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the… read more here.
Keywords: local graph; image sequences; cell; graph matching ... See more keywords
Joint Transformation Learning via the L2,1-Norm Metric for Robust Graph Matching
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2019.2912718
Abstract: Establishing correspondence between two given geometrical graph structures is an important problem in computer vision and pattern recognition. In this paper, we propose a robust graph matching (RGM) model to improve the effectiveness and robustness… read more here.
Keywords: robust graph; joint transformation; transformation learning; graph ... See more keywords