# A k-mer Based Approach for SARS-CoV-2 Variant Identification

arXiv: https://arxiv.org/abs/2108.03465 · HTML: https://mlsanity.com/paper/2108.03465/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Efficient Approximation Algorithms for String Kernel Based Sequence Classification](https://mlsanity.com/paper/1712.04264/) (2017)
- [Predicting Attributes of Nodes Using Network Structure](https://mlsanity.com/paper/1912.12264/) (2019)
- [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)
- [Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset](https://mlsanity.com/paper/2011.10188/) (2020)
- [Estimating Descriptors for Large Graphs](https://mlsanity.com/paper/2001.10301/) (2020)
- [Fair Allocation Based Soft Load Shedding](https://mlsanity.com/paper/2002.00451/) (2020)

## Built on by (most cited first, 23 of 23)

- [Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences](https://mlsanity.com/paper/2109.05019/) (2021)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022)
- [Effective and scalable clustering of SARS-CoV-2 sequences](https://mlsanity.com/paper/2108.08143/) (2021)
- [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://mlsanity.com/paper/2110.09622/) (2021)
- [Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data](https://mlsanity.com/paper/2211.08267/) (2022)
- [Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models](https://mlsanity.com/paper/2110.09606/) (2021)
- [Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification](https://mlsanity.com/paper/2207.08898/) (2022)
- [Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences](https://mlsanity.com/paper/2211.09263/) (2022)
- [Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning](https://mlsanity.com/paper/2302.08688/) (2023)
- [Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model](https://mlsanity.com/paper/2211.10546/) (2022)
- [Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location](https://mlsanity.com/paper/2110.00809/) (2021)
- [DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images](https://mlsanity.com/paper/2409.06694/) (2024)
- [Locally Weighted Mean Phase Angle (LWMPA) Based Tone Mapping Quality Index (TMQI-3)](https://mlsanity.com/paper/2109.08774/) (2021)
- [Unsupervised machine learning framework for discriminating major variants of concern during COVID-19](https://mlsanity.com/paper/2208.01439/) (2022)
- [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)
- [PCD2Vec: A Poisson Correction Distance-Based Approach for Viral Host Classification](https://mlsanity.com/paper/2304.06731/) (2023)
- [Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information](https://mlsanity.com/paper/2309.11294/) (2023)
- [A Misclassification Network-Based Method for Comparative Genomic Analysis](https://mlsanity.com/paper/2412.07051/) (2024)
- [Converting Time Series Data to Numeric Representations Using Alphabetic Mapping and k-mer strategy](https://mlsanity.com/paper/2412.20617/) (2024)
- [Breaking the Euclidean Barrier: Hyperboloid-Based Biological Sequence Analysis](https://mlsanity.com/paper/2510.01118/) (2025)
- [When do cheap embeddings beat protein language models? A theoretically-grounded hashing sketch for biological sequence classification](https://mlsanity.com/paper/2512.10147/) (2025)

## Abstract (arXiv)

With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2. Identifying particular variants helps to understand and model their spread patterns, design effective mitigation strategies, and prevent future outbreaks. It also plays a crucial role in studying the efficacy of known vaccines against each variant and modeling the likelihood of breakthrough infections. It is well known that the spike protein contains most of the information/variation pertaining to coronavirus variants. In this paper, we use spike sequences to classify different variants of the coronavirus in humans. We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance. We also show that we can train our model to outperform the baseline algorithms using only a small number of training samples (1% of the data). Finally, we show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC).