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

• Main Authors: Xin Hui Tay, Xin Hui Tay, Shahreen Kasim, Shahreen Kasim, Tole Sutikno, Tole Sutikno, Md Fudzee, Mohd Farhan, Hassan, Rohayanti, Patah Akhir, Emelia Akashah, Aziz, Norshakirah, Choon Sen Seah, Choon Sen Seah
• Format: Article
• Language: English
• Published: Mdpi 2023
• Subjects: T Technology (General)
• Online Access: http://eprints.uthm.edu.my/9586/1/J16097_fc692a6f023a80413e40b199966c0376.pdf; http://eprints.uthm.edu.my/9586/; https://doi.org/10.3390/genes14030574

The integration of microarray technologies and machine learning methods has become popular in predicting the pathological condition of diseases and discovering risk genes. Traditional microarray analysis considers pathways as a simple gene set, treating all genes in the pathway identically while ignoring the pathway network’s structure information. This study proposed an entropy-based directed random walk (e-DRW) method to infer pathway activities. Two enhancements from the conventional DRW were conducted, which are (1) to increase the coverage of human pathway information by constructing two inputting networks for pathway activity inference, and (2) to enhance the gene-weighting method in DRW by incorporating correlation coefficient values and t-test statistic scores. To test the objectives, gene expression datasets were used as input datasets while the pathway datasets were used as reference datasets to build two directed graphs. The withindataset experiments indicated that e-DRW method demonstrated robust and superior performance in terms of classification accuracy and robustness of the predicted risk-active pathways compared to the other methods. In conclusion, the results revealed that e-DRW not only improved the prediction performance, but also effectively extracted topologically important pathways and genes that were specifically related to the corresponding cancer types.