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Traditional pairwise alignment approaches are both time-consuming and memory-intensive. Alignment-free (AF) methods such as natural vector (NV) and k-mer operate on a one-dimensional framework, interpreting DNA primarily as a linear string of nucleotides. To achieve a more comprehensive interpretation of molecular structure, this study incorporates the three-dimensional architectural features of DNA and introduces a novel AF method named Multi-perspective natural vector (MNV). The MNV method maps genome sequences of varying lengths to points within a unified geometric space, facilitating large-size data processing tasks such as variant classification and clustering. Across datasets of different sizes and types, MNV attains a 100% convex hull separation ratio in lower dimensions compared with widely used methods NV and k-mer methods. In neural network classification, MNV achieves better classification accuracy of 99.55% and 98.78% on SARS-CoV-2 and poliovirus datasets respectively, demonstrating its effectiveness in viral genome analysis while maintaining computational efficiency.<\/jats:p>","DOI":"10.1177\/15578666251391211","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T10:49:17Z","timestamp":1765190957000},"page":"255-266","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Perspective Natural Vector: A Novel Method for Viral Sequence Feature Extraction"],"prefix":"10.1177","volume":"33","author":[{"given":"Xiang","family":"Shi","sequence":"first","affiliation":[{"name":"Department of Mathematical Sciences, Tsinghua University, Beijing, P. R. 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