eprintid: 18726
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/87/26
datestamp: 2024-06-04 14:11:07
lastmod: 2024-06-04 14:11:07
status_changed: 2024-06-04 14:03:56
type: article
metadata_visibility: show
creators_name: Tay, X.H.
creators_name: Kasim, S.
creators_name: Sutikno, T.
creators_name: Fudzee, M.F.M.
creators_name: Hassan, R.
creators_name: Patah Akhir, E.A.
creators_name: Aziz, N.
creators_name: Seah, C.S.
title: An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
ispublished: pub
keywords: area under the curve; Article; breast cancer; cancer classification; cancer prognosis; clinical feature; gene expression; genetic association; human; kidney cancer; liver cancer; lung cancer; risk factor; stomach cancer; thyroid cancer
note: cited By 2
abstract: 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 within-dataset 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. © 2023 by the authors.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152078530&doi=10.3390%2fgenes14030574&partnerID=40&md5=d663f024ba54347a08eb04740357c567
id_number: 10.3390/genes14030574
full_text_status: none
publication: Genes
volume: 14
number: 3
refereed: TRUE
citation:   Tay, X.H. and Kasim, S. and Sutikno, T. and Fudzee, M.F.M. and Hassan, R. and Patah Akhir, E.A. and Aziz, N. and Seah, C.S.  (2023) An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks.  Genes, 14 (3).