An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks
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 ign...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Mdpi
2023
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11562/1/J16097_fc692a6f023a80413e40b199966c0376.pdf |
| _version_ | 1825638567249969152 |
|---|---|
| author | Tay, Xin Hui Kasim, Shahreen Sutikno, Tole Md Fudzee, Mohd Farhan Hassan, Rohayanti Patah Akhir, Emelia Akashah Aziz, Norshakirah Seah, Choon Sen |
| author_facet | Tay, Xin Hui Kasim, Shahreen Sutikno, Tole Md Fudzee, Mohd Farhan Hassan, Rohayanti Patah Akhir, Emelia Akashah Aziz, Norshakirah Seah, Choon Sen |
| author_sort | Tay, Xin Hui |
| collection | UTHM |
| description | 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. |
| first_indexed | 2024-09-24T00:11:03Z |
| format | Article |
| id | uthm.eprints-11562 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-09-24T00:11:03Z |
| publishDate | 2023 |
| publisher | Mdpi |
| record_format | dspace |
| spelling | uthm.eprints-115622024-09-03T08:49:17Z http://eprints.uthm.edu.my/11562/ An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks Tay, Xin Hui Kasim, Shahreen Sutikno, Tole Md Fudzee, Mohd Farhan Hassan, Rohayanti Patah Akhir, Emelia Akashah Aziz, Norshakirah Seah, Choon Sen T Technology (General) 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. Mdpi 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/11562/1/J16097_fc692a6f023a80413e40b199966c0376.pdf Tay, Xin Hui and Kasim, Shahreen and Sutikno, Tole and Md Fudzee, Mohd Farhan and Hassan, Rohayanti and Patah Akhir, Emelia Akashah and Aziz, Norshakirah and Seah, Choon Sen (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 (574). pp. 1-13. https://doi.org/10.3390/genes14030574 |
| spellingShingle | T Technology (General) Tay, Xin Hui Kasim, Shahreen Sutikno, Tole Md Fudzee, Mohd Farhan Hassan, Rohayanti Patah Akhir, Emelia Akashah Aziz, Norshakirah Seah, Choon Sen An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks |
| title | An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks |
| title_full | An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks |
| title_fullStr | An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks |
| title_full_unstemmed | An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks |
| title_short | An Entropy-Based Directed Random Walk for Cancer Classification Using Gene Expression Data Based on Bi-Random Walk on Two Separated Networks |
| title_sort | entropy based directed random walk for cancer classification using gene expression data based on bi random walk on two separated networks |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/11562/1/J16097_fc692a6f023a80413e40b199966c0376.pdf |
| work_keys_str_mv | AT tayxinhui anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT kasimshahreen anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT sutiknotole anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT mdfudzeemohdfarhan anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT hassanrohayanti anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT patahakhiremeliaakashah anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT aziznorshakirah anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT seahchoonsen anentropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT tayxinhui entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT kasimshahreen entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT sutiknotole entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT mdfudzeemohdfarhan entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT hassanrohayanti entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT patahakhiremeliaakashah entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT aziznorshakirah entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks AT seahchoonsen entropybaseddirectedrandomwalkforcancerclassificationusinggeneexpressiondatabasedonbirandomwalkontwoseparatednetworks |