A New Method for Feature Extraction and Classification of Single-Stranded DNA Based on Collaborative Filter

Abstract

The traditional support vector machine algorithm is not enough to classify single-stranded DNA molecules, so this paper proposes an improved threshold extraction algorithm based on collaborative filter for the classification of single-stranded DNA. Firstly, according to the different characteristic curves of the blocking current signals formed by the four bases (A, T, C, and T) that make up DNA molecules crossing the nanopore, the collaborative filter feature extraction algorithm with improved threshold is proposed. Then, the feature information is reconstructed and sent to the SVM classifier for training. Finally, the unfiltered, collaborative filter, improved threshold collaborative filter, and Bessel filter data are, respectively, extracted and sent to the SVM classifier for classification and comparison research. The experimental results show that the improved collaborative filter algorithm has higher accuracy in single-stranded DNA molecular classification.