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- W2914148690 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." @default.
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- W2914148690 date "2019-02-01" @default.
- W2914148690 modified "2023-10-17" @default.
- W2914148690 title "Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination" @default.
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- W2914148690 doi "https://doi.org/10.2217/bmm-2018-0273" @default.
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