Graph 'texture' features as novel metrics that can summarize complex biological graphs.

ABSTRACT: Objective.Image texture features, such as those derived by Haralicket 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. T

SUBMITTER: Barker-Clarke R 

PROVIDER: S-EPMC10598684 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature