Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer
碩士 === 慈濟大學 === 醫學資訊學系碩士班 === 102 === According to the statistics record from World Health Organization, 521,000 global population dies from breast cancer in 2012. In clinical assessment of breast cancer, biomarkers become key criteria. MicroRNAs are a class of small non-coding (19 to 24 nucleotide)...
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ndltd-TW-102TCU006040132015-10-13T23:23:02Z http://ndltd.ncl.edu.tw/handle/60492176234309206732 Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer 探討乳癌微型核醣核酸生物標誌及建構仿真晶片檢測系統 Shu-Yi Huang 黃淑儀 碩士 慈濟大學 醫學資訊學系碩士班 102 According to the statistics record from World Health Organization, 521,000 global population dies from breast cancer in 2012. In clinical assessment of breast cancer, biomarkers become key criteria. MicroRNAs are a class of small non-coding (19 to 24 nucleotide) RNAs that regulate the expression of target mRNA at the post-transcriptional level. In recent years, many researches indicate that microRNAs play an important role in breast cancer. Discovering microRNA biomarkers of breast cancer is an increasingly aware issue. In this study, we use two filter methods of correlation, p-value combined with q-value, and one dimension method of PCA (Principal Component Analysis) to discover the potential microRNA biomarkers of breast cancer. According significant microRNAs of breast cancer, we use Decision Tree, ANN (Artificial Neural Network), GASVM (Genetic Algorithm Support Vector Machine) methods to develop simulated biochip detection system and construct visualization network platform to explore the significant microRNAs of breast cancer and their relationships with other human diseases. In this study, the accuracy of breast cancer classification with three different classifiers is: Decision Tree 62.25%, ANN 79.44% and GASVM 84.46%. We found that GASVM is the best classifier compared with the other two classifiers. In this thesis, we use both filter methods and dimension method to identify significant microRNA biomarkers of breast cancer.The results show that 17 microRNA biomarkers ( has-mir-22、has-mir-20b、has-mir-19b、has-mir-26a、has-mir-425、has-mir-30b、has-mir-20a、has-mir-106a、has-mir-17、has-mir-92a、has-mir-191、has-mir-15a、has-mir-93、has-mir-16、has-mir-25、has-mir-182、has-mir-7d ) are identified in breast cancer with biologic evidence and 4 microRNAs (has-mir-363、has-mir-194、has-mir-486-5p、has-mir-500) are potential biomarkers of breast cancer. According to the visualization network of breast cancer and other human diseases, we reveal that breast cancer and hepatocellular have the most significant associations. Keyword: microRNA, simulated biochip, biomarker, visualization Austin H Chen 陳信志 2014 學位論文 ; thesis 70 zh-TW |
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碩士 === 慈濟大學 === 醫學資訊學系碩士班 === 102 === According to the statistics record from World Health Organization, 521,000 global population dies from breast cancer in 2012. In clinical assessment of breast cancer, biomarkers become key criteria. MicroRNAs are a class of small non-coding (19 to 24 nucleotide) RNAs that regulate the expression of target mRNA at the post-transcriptional level. In recent years, many researches indicate that microRNAs play an important role in breast cancer. Discovering microRNA biomarkers of breast cancer is an increasingly aware issue.
In this study, we use two filter methods of correlation, p-value combined with q-value, and one dimension method of PCA (Principal Component Analysis) to discover the potential microRNA biomarkers of breast cancer. According significant microRNAs of breast cancer, we use Decision Tree, ANN (Artificial Neural Network), GASVM (Genetic Algorithm Support Vector Machine) methods to develop simulated biochip detection system and construct visualization network platform to explore the significant microRNAs of breast cancer and their relationships with other human diseases.
In this study, the accuracy of breast cancer classification with three different classifiers is: Decision Tree 62.25%, ANN 79.44% and GASVM 84.46%. We found that GASVM is the best classifier compared with the other two classifiers. In this thesis, we use both filter methods and dimension method to identify significant microRNA biomarkers of breast cancer.The results show that 17 microRNA biomarkers ( has-mir-22、has-mir-20b、has-mir-19b、has-mir-26a、has-mir-425、has-mir-30b、has-mir-20a、has-mir-106a、has-mir-17、has-mir-92a、has-mir-191、has-mir-15a、has-mir-93、has-mir-16、has-mir-25、has-mir-182、has-mir-7d ) are identified in breast cancer with biologic evidence and 4 microRNAs (has-mir-363、has-mir-194、has-mir-486-5p、has-mir-500) are potential biomarkers of breast cancer. According to the visualization network of breast cancer and other human diseases, we reveal that breast cancer and hepatocellular have the most significant associations.
Keyword: microRNA, simulated biochip, biomarker, visualization
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author2 |
Austin H Chen |
author_facet |
Austin H Chen Shu-Yi Huang 黃淑儀 |
author |
Shu-Yi Huang 黃淑儀 |
spellingShingle |
Shu-Yi Huang 黃淑儀 Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer |
author_sort |
Shu-Yi Huang |
title |
Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer |
title_short |
Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer |
title_full |
Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer |
title_fullStr |
Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer |
title_full_unstemmed |
Exploration of MicroRNA Biomakers and Development of Simulated Biochip System in Breast Cancer |
title_sort |
exploration of microrna biomakers and development of simulated biochip system in breast cancer |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/60492176234309206732 |
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