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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

Graph kernels combined with the neural network on protein classification.

Jiang Qiangrong¹, Qiu Guang¹

¹Department of Computer Science, Beijing University of Technology, Beijing, P. R. China.

Journal of Bioinformatics and Computational Biology

|December 21, 2019

Summary

This study introduces a novel vertex-edge similarity kernel (VES kernel) for protein classification. The new graph kernel method improves accuracy when combined with neural networks, outperforming existing approaches.

Keywords:

Protein classification graph kernel mixed matrix neural network

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Protein classification is crucial in bioinformatics.
Current methods often rely on graph kernels to analyze protein structures.
Existing graph kernels focus on substructure similarity.

Purpose of the Study:

To propose a novel graph kernel for protein classification.
To introduce the vertex-edge similarity kernel (VES kernel).
To enhance protein classification accuracy using machine learning.

Main Methods:

Developed a novel vertex-edge similarity kernel (VES kernel).
Utilized mixed matrices and adjacency matrices as vertex sample vectors.
Calculated kernel values based on the most similar vertex pairs between graphs.
Integrated the VES kernel with neural networks.

Main Results:

The proposed VES kernel effectively captures graph similarities.
Combining the VES kernel with neural networks significantly improved protein classification performance.
Experimental results demonstrated superior performance compared to existing advanced methods.

Conclusions:

The vertex-edge similarity kernel offers a promising new approach for protein classification.
The integration of VES kernel with neural networks represents an advancement in the field.
This method provides a more effective way to analyze protein structures for classification.