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Related Experiment Video

Updated: Jul 25, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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

R Barker-Clarke¹, D T Weaver^1,2, J G Scott^1,2

¹Department of Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland, OH 44195, United States of America.

Physics in Medicine and Biology

|June 29, 2023

Summary

This summary is machine-generated.

Researchers developed novel graph texture features, analogous to image texture analysis, to analyze biological networks. These features effectively classify cancer cell lineage, offering new tools for network science and cancer informatics.

Keywords:

GLCM fitness landscapes gene expression graphs networks texture topology

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Area of Science:

Network Science
Bioinformatics
Computational Biology

Background:

Image texture features are valuable for image classification, including in cancer research.
Analyzing complex biological networks requires advanced quantitative methods.

Purpose of the Study:

To derive graph texture features analogous to image texture analysis.
To demonstrate their utility in summarizing, comparing, and classifying biological networks, particularly in cancer research.

Main Methods:

Generated graph co-occurrence matrices by summing over neighboring node pairs.
Developed and applied texture metrics to fitness landscapes, gene networks, and protein interaction networks.
Assessed metric sensitivity to discretization and noise; built random forest classifiers for cancer cell lineage.

Main Results:

Novel graph texture features effectively capture graph structure and node label distributions.
Metrics demonstrated sensitivity to parameter variations and noise.
Classifiers achieved 82% and 89% accuracy in distinguishing cancer cell line expression by lineage.

Conclusions:

New graph texture features offer novel second-order network metrics for comparative analysis and classification.
These features show promise for applications in cancer informatics, such as evolutionary analysis and drug response prediction.