A pattern recognition model to distinguish cancerous DNA sequences via signal processing methods

Cancer is one of the life-threatening diseases caused by changes in the structure of genetic components of the cell. DNA sequences are one of the most important factors in the formation and spread of this disease. The signal processing approach is one of the scientific fields that has been developed in the last two decades in the analysis of DNA sequences. In this research, a hybrid model of discrete Fourier transform and anti-notch digital filter has been used for this purpose. The aim of using these techniques is to model an approach that can distinguish cancerous samples from non-cancerous ones. In other words, a pattern recognition model is designed to discriminate cancerous cell samples based on the features of protein coding regions of DNA sequences. Some computational and statistical techniques have been used in feature extraction and feature selection stages. Despite the proposed model simplicity, it doesn't face conventional challenges such as high computational complexity or memory dissipation. Case studies have been tested with the least possible feature, depending on the nature of the features. Experimental results and features relationship led to the proposal of the SVM classifier to discriminate two categories. The output features and classification show good discrimination results among the cancerous and non-cancerous samples. One of the main advantages of the proposed model is the independence of its performance over the data length. Evaluation and validation results indicate the high accuracy and precision of the proposed method which emphasizes the biological genetic mutation nature of cancer.