Ensemble based biomarker identification on pancreatic ductal adenocarcinoma gene expressions

Abstract

Malignancy order is an imperative advance in biomarker distinguishing proof. Creating machine learning techniques that effectively anticipate tumor subtypes can help in distinguishing potential disease biomarkers. In this critique, we have introduced ensemble classification approach and contrasted its execution with other characterization approaches. PDAC microarray-based gene expression given in Gene Expression Omnibus (GEO) datasets are analyzed. After preprocessing of data, we have classified using Bagged tree Ensemble method and compared with other classifiers. The general achievement rate hence acquired is average of 96.64% for five testing datasets. Such a rate is 6–15% higher than the comparing rates obtained by different existing DT (decision tree), DA (discriminant analysis) and SVM (support vector machines) and NN (nearest neighbor) approaches, inferring that the gathering classifier is exceptionally encouraging and may turn into a significant test for biomarker identification. Finally, the biological analysis has been done to detect the common biomarkers for PDAC.