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Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination.

ABSTRACT: AIM:Pancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important. RESULTS:We put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms. As a result, seven differentially expressed genes were predicted as specific biomarkers for pancreatic cancer because of their highest accuracy of classification on cancer and normal samples. CONCLUSION:Three (MMP7, FOS and A2M) out of the seven predicted gene markers were found to encode proteins secreted into urine, providing potential diagnostic evidences for pancreatic cancer.

SUBMITTER: Wang Y 

PROVIDER: S-EPMC6737501 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination.

Wang Yan Y   Liu Keke K   Ma Qin Q   Tan Yongfei Y   Du Wei W   Lv Yidan Y   Tian Yuan Y   Wang Hao H  

Biomarkers in medicine 20190215 2

<h4>Aim</h4>Pancreatic cancer is one of the worst malignant tumors in prognosis. Therefore, to reduce the mortality rate of pancreatic cancer, early diagnosis and prompt treatment are particularly important.<h4>Results</h4>We put forward a new feature-selection method that was used to find clinical markers for pancreatic cancer by combination of Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Large Margin Distribution Machine Recursive Feature Elimination (LDM-RFE) algorithms.  ...[more]

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