Biomimetic Pattern Recognition Method for Breast Cancer Using Gene Expression Data

Abstract

The purpose of this study was to develop a novel prediction method for breast cancer based on gene expression data through using a susceptible marker-selectable biomimetic pattern recognition (BPR) method, with which a parameter increasing method (PIM) was proposed to incorporate. The method was used to predict early detection, transition from normal cell to cancerous cell and prognosis signature of patients with adjuvant systemic therapy. Several genes were selected as susceptible genes associated with breast cancer. It can be shown by the results that the “cognition” BPR method could correctly predict detection, cancerous cell transition and good or poor prognosis signature with approximate 85%, 98% and 88% accuracy separately. In order to study the performance of BPR, Fisher discriminant analysis (FDA) and support vector machine (SVM) methods also were applied to analyze the gene expression data. From the results, it can be found that the BPR method is superior to FDA and SVM with respect to classification ability. Furthermore, the prediction performance can be improved through using biomarker instead of whole gene expression data for any method.