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An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on

Xin Hui Tay¹, Shahreen Kasim¹, Tole Sutikno²

¹Faculty of Computer Sciences and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat 83000, Malaysia.

|March 29, 2023

Summary

This study introduces an entropy-based directed random walk (e-DRW) method for improved disease prediction using gene expression data. The e-DRW method enhances pathway activity inference, leading to more accurate identification of cancer-related pathways and genes.

Keywords:

cancer classification directed random walk pathway-based analysis

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray technology and machine learning are crucial for disease prediction and risk gene discovery.
Traditional methods overlook pathway network structure, treating pathways as simple gene sets.

Purpose of the Study:

To propose an entropy-based directed random walk (e-DRW) method for inferring pathway activities.
To enhance pathway analysis by incorporating network structure and improved gene weighting.

Main Methods:

Developed an e-DRW method with two key enhancements: expanded human pathway information coverage and refined gene weighting using correlation coefficients and t-test scores.
Utilized gene expression datasets as input and pathway datasets to construct directed graphs for analysis.

Main Results:

The e-DRW method demonstrated robust and superior classification accuracy compared to existing methods.
Experiments confirmed the robustness of predicted risk-active pathways.

Conclusions:

The e-DRW method significantly improves prediction performance in disease pathology.
Successfully identified topologically important pathways and genes specific to cancer types.