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Updated: Mar 19, 2026

Isolation of Intermediate Filament Proteins from Multiple Mouse Tissues to Study Aging-associated Post-translational Modifications

Isolation of Intermediate Filament Proteins from Multiple Mouse Tissues to Study Aging-associated Post-translational Modifications

Published on: May 18, 2017

New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins.

Fabio Fabris¹, Alex A Freitas¹

¹School of Computing, University of Kent, CT2 7NF Canterbury, Kent, UK.

Bioinformatics (Oxford, England)

|June 19, 2016

Summary

This study introduces novel interpretable features from Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for analyzing protein data related to aging. These features improve predictive accuracy in classification tasks, aiding experts in understanding aging mechanisms.

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

Increasing incidence of aging-related diseases necessitates advanced methods for analyzing protein data.
Effective analysis requires interpretable classification models and biologically meaningful features.
Current methods may lack the interpretability and predictive accuracy needed for aging research.

Purpose of the Study:

To propose and evaluate two novel interpretable feature types derived from Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for analyzing protein data.
To compare the performance of KEGG-based features against traditional features in both hierarchical and binary classification tasks.
To develop a method for interpreting classification models built using KEGG features.

Main Methods:

Development of two novel feature types based on KEGG pathways, including one utilizing the graph structure to quantify protein influence.
Application of these features in hierarchical and binary classification tasks using datasets with labels from the Mouse Phenotype Ontology.
Comparison of predictive accuracy across different feature types and classification algorithms.

Main Results:

One KEGG feature type achieved the highest predictive accuracy among five individual feature types in hierarchical classification.
The combination of the two proposed KEGG feature types yielded top-tier predictive accuracy in binary classification.
The study demonstrated the ability to extract quantitative influence information from aging-related data.

Conclusions:

KEGG pathway-based features offer a promising approach for enhancing the analysis of protein data in aging research.
The proposed features provide both high predictive accuracy and interpretability, crucial for domain experts.
This work pioneers the use of KEGG pathway graph structures for feature engineering in aging-related classification.