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Graph ‘texture’ features as novel metrics that can summarize complex biological graphs

Objective. Image texture features, such as those derived by Haralick et al, are a powerful metric for image classification and are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate...

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Autores principales: Barker-Clarke, R, Weaver, D T, Scott, J G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598684/
https://www.ncbi.nlm.nih.gov/pubmed/37385267
http://dx.doi.org/10.1088/1361-6560/ace305
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author Barker-Clarke, R
Weaver, D T
Scott, J G
author_facet Barker-Clarke, R
Weaver, D T
Scott, J G
author_sort Barker-Clarke, R
collection PubMed
description Objective. Image texture features, such as those derived by Haralick et al, are a powerful metric for image classification and are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate how these new metrics summarize graphs, may aid comparative graph studies, may help classify biological graphs, and might assist in detecting dysregulation in cancer. Approach. We generate the first analogies of image texture for graphs and networks. Co-occurrence matrices for graphs are generated by summing over all pairs of neighboring nodes in the graph. We generate metrics for fitness landscapes, gene co-expression and regulatory networks, and protein interaction networks. To assess metric sensitivity we varied discretization parameters and noise. To examine these metrics in the cancer context we compare metrics for both simulated and publicly available experimental gene expression and build random forest classifiers for cancer cell lineage. Main results. Our novel graph ‘texture’ features are shown to be informative of graph structure and node label distributions. The metrics are sensitive to discretization parameters and noise in node labels. We demonstrate that graph texture features vary across different biological graph topologies and node labelings. We show how our texture metrics can be used to classify cell line expression by lineage, demonstrating classifiers with 82% and 89% accuracy. Significance. New metrics provide opportunities for better comparative analyzes and new models for classification. Our texture features are novel second-order graph features for networks or graphs with ordered node labels. In the complex cancer informatics setting, evolutionary analyses and drug response prediction are two examples where new network science approaches like this may prove fruitful.
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spelling pubmed-105986842023-10-26 Graph ‘texture’ features as novel metrics that can summarize complex biological graphs Barker-Clarke, R Weaver, D T Scott, J G Phys Med Biol Paper Objective. Image texture features, such as those derived by Haralick et al, are a powerful metric for image classification and are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate how these new metrics summarize graphs, may aid comparative graph studies, may help classify biological graphs, and might assist in detecting dysregulation in cancer. Approach. We generate the first analogies of image texture for graphs and networks. Co-occurrence matrices for graphs are generated by summing over all pairs of neighboring nodes in the graph. We generate metrics for fitness landscapes, gene co-expression and regulatory networks, and protein interaction networks. To assess metric sensitivity we varied discretization parameters and noise. To examine these metrics in the cancer context we compare metrics for both simulated and publicly available experimental gene expression and build random forest classifiers for cancer cell lineage. Main results. Our novel graph ‘texture’ features are shown to be informative of graph structure and node label distributions. The metrics are sensitive to discretization parameters and noise in node labels. We demonstrate that graph texture features vary across different biological graph topologies and node labelings. We show how our texture metrics can be used to classify cell line expression by lineage, demonstrating classifiers with 82% and 89% accuracy. Significance. New metrics provide opportunities for better comparative analyzes and new models for classification. Our texture features are novel second-order graph features for networks or graphs with ordered node labels. In the complex cancer informatics setting, evolutionary analyses and drug response prediction are two examples where new network science approaches like this may prove fruitful. IOP Publishing 2023-09-07 2023-08-22 /pmc/articles/PMC10598684/ /pubmed/37385267 http://dx.doi.org/10.1088/1361-6560/ace305 Text en © 2023 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Barker-Clarke, R
Weaver, D T
Scott, J G
Graph ‘texture’ features as novel metrics that can summarize complex biological graphs
title Graph ‘texture’ features as novel metrics that can summarize complex biological graphs
title_full Graph ‘texture’ features as novel metrics that can summarize complex biological graphs
title_fullStr Graph ‘texture’ features as novel metrics that can summarize complex biological graphs
title_full_unstemmed Graph ‘texture’ features as novel metrics that can summarize complex biological graphs
title_short Graph ‘texture’ features as novel metrics that can summarize complex biological graphs
title_sort graph ‘texture’ features as novel metrics that can summarize complex biological graphs
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10598684/
https://www.ncbi.nlm.nih.gov/pubmed/37385267
http://dx.doi.org/10.1088/1361-6560/ace305
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