Premalignant pancreatic cancer diagnosis using proteomic pattern analysis
Pancreatic cancer is one of the deadliest cancers due to the fact that it does not exhibit symptoms in the early stages. Furthermore, when pancreatic cancer gets diagnosed, it is usually too late. Consequently, early diagnosis is highly essential. The dawn of proteomics has brought with it a glimps...
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Engineering and Technology Publishing
2015
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my.iium.irep.399022016-11-07T08:12:57Z http://irep.iium.edu.my/39902/ Premalignant pancreatic cancer diagnosis using proteomic pattern analysis Htike@Muhammad Yusof, Zaw Zaw T Technology (General) Pancreatic cancer is one of the deadliest cancers due to the fact that it does not exhibit symptoms in the early stages. Furthermore, when pancreatic cancer gets diagnosed, it is usually too late. Consequently, early diagnosis is highly essential. The dawn of proteomics has brought with it a glimpse of hope of uncovering biomarkers that can be indicative of early pancreatic cancer. Proteome profiling techniques have become popular in the recent years to try to make sense of high-dimensional proteomic data and to find discrepancies between proteomes of healthy samples and cancerous samples. However, the high dimensionality of proteomics data coupled with small sample size poses a challenge. In this paper, we propose a framework using a hybrid logistic tree technique together with a feature selection technique to diagnose premalignant pancreatic cancer. We have validated our framework on a pancreatic cancer peptide mass spectrometry dataset. Satisfactory preliminary experimental results demonstrate the efficacy of our framework. Engineering and Technology Publishing 2015-08 Article REM application/pdf en http://irep.iium.edu.my/39902/1/20141114105404326.pdf Htike@Muhammad Yusof, Zaw Zaw (2015) Premalignant pancreatic cancer diagnosis using proteomic pattern analysis. Journal of Medical and Bioengineering, 4 (4). pp. 288-292. ISSN 2301-3796 http://www.jomb.org/index.php?m=content&c=index&a=lists&catid=46 |
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T Technology (General) Htike@Muhammad Yusof, Zaw Zaw Premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
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Pancreatic cancer is one of the deadliest cancers due to the fact that it does not exhibit symptoms in the early stages. Furthermore, when pancreatic cancer gets diagnosed, it is usually too late. Consequently, early diagnosis is highly essential. The dawn of proteomics has brought with it a glimpse of hope of uncovering biomarkers that can be indicative of early pancreatic cancer. Proteome profiling techniques have become popular in the recent years to try to make sense of high-dimensional proteomic data and to find discrepancies between proteomes of healthy samples and cancerous samples. However, the high dimensionality of proteomics data coupled with small sample size poses a challenge. In this paper, we propose a framework using a hybrid logistic tree technique together with a feature selection technique to diagnose premalignant pancreatic cancer. We have validated our framework on a pancreatic cancer peptide mass spectrometry dataset. Satisfactory preliminary experimental results demonstrate the efficacy of our framework. |
| format |
Article |
| author |
Htike@Muhammad Yusof, Zaw Zaw |
| author_facet |
Htike@Muhammad Yusof, Zaw Zaw |
| author_sort |
Htike@Muhammad Yusof, Zaw Zaw |
| title |
Premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
| title_short |
Premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
| title_full |
Premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
| title_fullStr |
Premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
| title_full_unstemmed |
Premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
| title_sort |
premalignant pancreatic cancer diagnosis using proteomic pattern analysis |
| publisher |
Engineering and Technology Publishing |
| publishDate |
2015 |
| url |
http://irep.iium.edu.my/39902/1/20141114105404326.pdf http://irep.iium.edu.my/39902/ http://www.jomb.org/index.php?m=content&c=index&a=lists&catid=46 |
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