DMD-CGR: Dynamic Mode Decomposition-based Novel Features for DNA sequence classification

Accurate classification and analysis of pathogenic organisms are essential for understanding the evolutionary relationships between different viruses, as well as for developing effective treatments and preventive measures. In this paper, we propose the use of machine learning models for pathogen classification, specifically for classifying a novel pathogen at a genus level. We propose a novel feature extraction technique called Dynamic Coherent Features through Progressive Rank Approximations (DCFPRA) for extracting relevant features from genomic sequences. We then evaluate the performance of several machine learning algorithms, including Support Vector Machines (SVM), and K-Nearest Neighbours (KNN), using these features. Our results demonstrate that machine learning models trained on DCFPRA features outperform traditional alignment-based methods for pathogen classification. Furthermore, our proposed method is computationally efficient and can handle large-scale datasets, making it a valuable tool for future research in pathogen classification.