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Updated: Jan 29, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
Published on: March 1, 2024
Pancreatic cancer biomarker detection by two support vector strategies for recursive feature elimination
Yan Wang1,2, Keke Liu1, Qin Ma3
1Key Laboratory of Symbol Computation & Knowledge Engineering of Ministry of Education, College of Computer Science & Technology, Jilin University, Changchun 130012, PR China.
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.
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