Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs

Nirmala, P.; Nadarajan, R.

doi:10.1016/j.molstruc.2021.130980

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…In recent years, centrality measures have found several applications in chemistry. For example, eigenvector centrality has been applied to the study of certain molecular substructures in [53], whereas closeness and betweenness centrality have been used to show that osmolytes act as hubs in the hydrogen-bond network of the solution [60].…”

Section: Centrality Measuresmentioning

confidence: 99%

Structural analysis of water networks

Benzi

Daidone

Faccio

2022

Journal of Complex Networks

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Liquid water, besides being fundamental for life on Earth, has long fascinated scientists due to several anomalies. Different hypotheses have been put forward to explain these peculiarities. The most accredited one foresees the presence in the supercooled region of two phases at different densities: the low-density liquid phase and the high-density liquid phase. In our previous work [Faccio et al. (2022), J. Mol. Liq., 355, 118922], we showed that it is possible to identify these two forms in water networks through a computational approach based on molecular dynamics simulation and on the calculation of the total communicability of the associated graph, in which the nodes correspond to water molecules and the edges represent the connections (interactions) between molecules. In this article, we present a more in-depth investigation of the application of graph-theory based approaches to the analysis of the structure of water networks. In particular, we investigate different connectivity and centrality measures and we report on the use of a variety of global metrics aimed at giving a topological and geometrical characterization of liquid water.

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Section: Centrality Measuresmentioning

confidence: 99%

Structural analysis of water networks

Benzi

Daidone

Faccio

2022

Journal of Complex Networks

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“…Eigenvector centrality (EC). Eigenvector centrality (EC) 77 scores correspond to the values of the first eigenvector of the graph adjacency matrix. The EC score of u is defined as:…”

Section: Benchmarking Quantification and Statisticsmentioning

confidence: 99%

Single-cell biological network inference using a heterogeneous graph transformer

Wang

et al. 2023

Nat Commun

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Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

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“…Eigenvector centrality has been utilized for assessing heterogeneity and damage in proteins based on polymer networks [7], for finding a maximal common connected vertex induced molecular substructure of two chemical graphs [12], and for functional importance and diversity of yeast proteins in the context of a protein-protein interaction network [13]. A molecule can be visualized as a graph and also represented as an undirected, labeled graph in which atoms are nodes and bonds are the edges of the graph.…”

Section: Conclusion and Remarksmentioning

confidence: 99%

Molecular Graph Indexes for Assessing Heterogeneity of Chemical Compounds

Wakakuri,

Taguchi,

Koge

et al. 2023

J. Comput. Aided Chem.

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Chemical graphs are utilized to predict various physical properties of molecules. A molecule can be represented as an undirected, labeled graph in which atoms are nodes and bonds are the edges of the graph. In this paper, we defined two indexes for a molecule called HG1 and HG2 based on the variance of elements in the eigenvector corresponding to the highest eigenvalue of the adjacency matrix of the molecular graph. We calculated and examined HG1 and HG2 of a huge number of natural products listed in KNApSAcK database. Heterogeneities of molecules can be assessed based on HG1 and HG2 but HG1 is more suitable for this purpose.

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Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs

Cited by 7 publications

References 18 publications

Structural analysis of water networks

Structural analysis of water networks

Single-cell biological network inference using a heterogeneous graph transformer

Molecular Graph Indexes for Assessing Heterogeneity of Chemical Compounds

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