An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets
Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed rando...
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| Format: | Article |
| Language: | English |
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ScienceDirect
2017
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/1/paper.pdf |
| _version_ | 1848820998333267968 |
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| author | Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail |
| author_facet | Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail |
| author_sort | Choon, Sen Seah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway data- set is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between signifi- cant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification. |
| first_indexed | 2025-11-15T02:18:21Z |
| format | Article |
| id | ump-19999 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:18:21Z |
| publishDate | 2017 |
| publisher | ScienceDirect |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-199992018-03-27T03:20:43Z http://umpir.ump.edu.my/id/eprint/19999/ An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail QA75 Electronic computers. Computer science Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway data- set is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between signifi- cant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification. ScienceDirect 2017-11-20 Article PeerReviewed application/pdf en cc_by_nc_nd http://umpir.ump.edu.my/id/eprint/19999/1/paper.pdf Choon, Sen Seah and Shahreen, Kasim and Mohd Farhan, Md Fudzee and Jeffrey Mark, Law Tze Ping and Mohd Saberi, Mohamad and Saedudin, Rd Rohmat and Mohd Arfian, Ismail (2017) An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets. Saudi Journal of Biological Sciences, 24 (8). pp. 1828-1841. ISSN 1319-562X. (Published) https://doi.org/10.1016/j.sjbs.2017.11.024 doi: 10.1016/j.sjbs.2017.11.024 |
| spellingShingle | QA75 Electronic computers. Computer science Choon, Sen Seah Shahreen, Kasim Mohd Farhan, Md Fudzee Jeffrey Mark, Law Tze Ping Mohd Saberi, Mohamad Saedudin, Rd Rohmat Mohd Arfian, Ismail An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets |
| title | An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets |
| title_full | An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets |
| title_fullStr | An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets |
| title_full_unstemmed | An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets |
| title_short | An Enhanced Topologically Significant Directed Random Walk in Cancer Classification using Gene Expression Datasets |
| title_sort | enhanced topologically significant directed random walk in cancer classification using gene expression datasets |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/ http://umpir.ump.edu.my/id/eprint/19999/1/paper.pdf |