# Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location

arXiv: https://arxiv.org/abs/2110.00809 · HTML: https://mlsanity.com/paper/2110.00809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 2 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [A k-mer Based Approach for SARS-CoV-2 Variant Identification](https://mlsanity.com/paper/2108.03465/) (2021)
- [Efficient Approximation Algorithms for String Kernel Based Sequence Classification](https://mlsanity.com/paper/1712.04264/) (2017)
- [Approximated and User Steerable tSNE for Progressive Visual Analytics](https://mlsanity.com/paper/1512.01655/) (2015)
- [Predicting Attributes of Nodes Using Network Structure](https://mlsanity.com/paper/1912.12264/) (2019)
- [Effective and scalable clustering of SARS-CoV-2 sequences](https://mlsanity.com/paper/2108.08143/) (2021)
- [Detecting DDoS Attack on SDN Due to Vulnerabilities in OpenFlow](https://mlsanity.com/paper/1912.12221/) (2019)
- [Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal](https://mlsanity.com/paper/2002.00461/) (2020)
- [Short-Term Load Forecasting Using AMI Data](https://mlsanity.com/paper/1912.12479/) (2019)
- [Combinatorial Trace Method for Network Immunization](https://mlsanity.com/paper/1912.12070/) (2019)
- [A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts](https://mlsanity.com/paper/1912.12068/) (2019)
- [Language Independent Sentiment Analysis](https://mlsanity.com/paper/1912.11973/) (2019)
- [Spectral Methods for Immunization of Large Networks](https://mlsanity.com/paper/1711.00791/) (2017)
- [Scalable Approximation Algorithm for Network Immunization](https://mlsanity.com/paper/1711.00784/) (2017)
- [A Multi-cascaded Deep Model for Bilingual SMS Classification](https://mlsanity.com/paper/1911.13066/) (2019)
- [Estimating Descriptors for Large Graphs](https://mlsanity.com/paper/2001.10301/) (2020)
- [Computing Graph Descriptors on Edge Streams](https://mlsanity.com/paper/2109.01494/) (2021)

## Built on by (most cited first, 2 of 2)

- [BioSequence2Vec: Efficient Embedding Generation For Biological Sequences](https://mlsanity.com/paper/2304.00291/) (2023)
- [ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation](https://mlsanity.com/paper/2304.02891/) (2023)

## Abstract (arXiv)

With the rapid spread of COVID-19 worldwide, viral genomic data is available in the order of millions of sequences on public databases such as GISAID. This Big Data creates a unique opportunity for analysis towards the research of effective vaccine development for current pandemics, and avoiding or mitigating future pandemics. One piece of information that comes with every such viral sequence is the geographical location where it was collected -- the patterns found between viral variants and geographical location surely being an important part of this analysis. One major challenge that researchers face is processing such huge, highly dimensional data to obtain useful insights as quickly as possible. Most of the existing methods face scalability issues when dealing with the magnitude of such data. In this paper, we propose an approach that first computes a numerical representation of the spike protein sequence of SARS-CoV-2 using k-mers (substrings) and then uses several machine learning models to classify the sequences based on geographical location. We show that our proposed model significantly outperforms the baselines. We also show the importance of different amino acids in the spike sequences by computing the information gain corresponding to the true class labels.