Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method
Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene priori...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1100/2012/842727 |
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| author | Carlos Roberto Arias Hsiang-Yuan Yeh Von-Wun Soo |
| author_facet | Carlos Roberto Arias Hsiang-Yuan Yeh Von-Wun Soo |
| author_sort | Carlos Roberto Arias |
| collection | DOAJ |
| description | Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well. |
| format | Article |
| id | doaj-art-4c4e332bbfc444d89feba1b310c22212 |
| institution | DOAJ |
| issn | 1537-744X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4c4e332bbfc444d89feba1b310c222122025-08-20T03:20:13ZengWileyThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/842727842727Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization MethodCarlos Roberto Arias0Hsiang-Yuan Yeh1Von-Wun Soo2Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, TaiwanComputer Science Department, National Tsing Hua University, Hsinchu 30013, TaiwanInstitute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, TaiwanFinding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.http://dx.doi.org/10.1100/2012/842727 |
| spellingShingle | Carlos Roberto Arias Hsiang-Yuan Yeh Von-Wun Soo Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method The Scientific World Journal |
| title | Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method |
| title_full | Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method |
| title_fullStr | Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method |
| title_full_unstemmed | Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method |
| title_short | Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method |
| title_sort | biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method |
| url | http://dx.doi.org/10.1100/2012/842727 |
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