# Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences

arXiv: https://arxiv.org/abs/2109.05019 · HTML: https://mlsanity.com/paper/2109.05019/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 12 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Random Feature Attention](https://mlsanity.com/paper/2103.02143/) (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)
- [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)
- [Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy](https://mlsanity.com/paper/1704.04650/) (2017)
- [Estimating Descriptors for Large Graphs](https://mlsanity.com/paper/2001.10301/) (2020)
- [Cache Replacement Algorithm](https://mlsanity.com/paper/2107.14646/) (2021)
- [Computing Graph Descriptors on Edge Streams](https://mlsanity.com/paper/2109.01494/) (2021)

## Built on by (most cited first, 30 of 30)

- [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)
- [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)
- [Exploring The Potential Of GANs In Biological Sequence Analysis](https://mlsanity.com/paper/2303.02421/) (2023)
- [Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model](https://mlsanity.com/paper/2211.10546/) (2022)
- [T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification](https://mlsanity.com/paper/2304.13145/) (2023)
- [Characterizing SARS-CoV-2 Spike Sequences Based on Geographical Location](https://mlsanity.com/paper/2110.00809/) (2021)
- [Anderson Acceleration For Bioinformatics-Based Machine Learning](https://mlsanity.com/paper/2302.00347/) (2023)
- [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)
- [Information We Can Extract About a User From 'One Minute Mobile Application Usage'](https://mlsanity.com/paper/2207.13222/) (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)
- [Virus2Vec: Viral Sequence Classification Using Machine Learning](https://mlsanity.com/paper/2304.12328/) (2023)
- [Nearest Neighbor CCP-Based Molecular Sequence Analysis](https://mlsanity.com/paper/2409.04922/) (2024)
- [Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks](https://mlsanity.com/paper/2410.12655/) (2024)
- [MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering](https://mlsanity.com/paper/2410.15688/) (2024)
- [Sequence Analysis Using the Bezier Curve](https://mlsanity.com/paper/2503.14574/) (2025)
- [Sequence-Based Nanobody-Antigen Binding Prediction](https://mlsanity.com/paper/2308.01920/) (2023)
- [Beyond Accuracy: Measuring Representation Capacity of Embeddings to Preserve Structural and Contextual Information](https://mlsanity.com/paper/2309.11294/) (2023)
- [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)
- [Sequence-to-Image Transformation for Sequence Classification Using Rips Complex Construction and Chaos Game Representation](https://mlsanity.com/paper/2512.10141/) (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 global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is currently several million, and is increasing with every day. The availability of such Big Data creates a new opportunity for researchers to study this virus in detail. This is particularly important with all of the dynamics of the COVID-19 variants which emerge and circulate. This rich data source will give us insights on the best ways to perform genomic surveillance for this and future pandemic threats, with the ultimate goal of mitigating or eliminating such threats. Analyzing and processing the several million genomic sequences is a challenging task. Although traditional methods for sequence classification are proven to be effective, they are not designed to deal with these specific types of genomic sequences. Moreover, most of the existing methods also face the issue of scalability. Previous studies which were tailored to coronavirus genomic data proposed to use spike sequences (corresponding to a subsequence of the genome), rather than using the complete genomic sequence, to perform different machine learning (ML) tasks such as classification and clustering. However, those methods suffer from scalability issues. In this paper, we propose an approach called Spike2Vec, an efficient and scalable feature vector representation for each spike sequence that can be used for downstream ML tasks. Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc.