# SARS-Cov-2 RNA Sequence Classification Based on Territory Information

arXiv: https://arxiv.org/abs/2101.03323 · HTML: https://mlsanity.com/paper/2101.03323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19](https://mlsanity.com/paper/2010.14004/) (2020)
- [Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm](https://mlsanity.com/paper/1904.09061/) (2019)

## Abstract (arXiv)

CovID-19 genetics analysis is critical to determine virus type,virus variant and evaluate vaccines. In this paper, SARS-Cov-2 RNA sequence analysis relative to region or territory is investigated. A uniform framework of sequence SVM model with various genetics length from short to long and mixed-bases is developed by projecting SARS-Cov-2 RNA sequence to different dimensional space, then scoring it according to the output probability of pre-trained SVM models to explore the territory or origin information of SARS-Cov-2. Different sample size ratio of training set and test set is also discussed in the data analysis. Two SARS-Cov-2 RNA classification tasks are constructed based on GISAID database, one is for mainland, Hongkong and Taiwan of China, and the other is a 6-class classification task (Africa, Asia, Europe, North American, South American& Central American, Ocean) of 7 continents. For 3-class classification of China, the Top-1 accuracy rate can reach 82.45% (train 60%, test=40%); For 2-class classification of China, the Top-1 accuracy rate can reach 97.35% (train 80%, test 20%); For 6-class classification task of world, when the ratio of training set and test set is 20% : 80% , the Top-1 accuracy rate can achieve 30.30%. And, some Top-N results are also given.