# Efficient Approximate Kernel Based Spike Sequence Classification

arXiv: https://arxiv.org/abs/2209.04952 · HTML: https://mlsanity.com/paper/2209.04952/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [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)
- [PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences](https://mlsanity.com/paper/2201.02273/) (2022)
- [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)
- [Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants](https://mlsanity.com/paper/2110.09622/) (2021)
- [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)
- [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)
- [Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models](https://mlsanity.com/paper/2110.09606/) (2021)

## Built on by (most cited first, 18 of 18)

- [Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data](https://mlsanity.com/paper/2211.08267/) (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)
- [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)
- [A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis](https://mlsanity.com/paper/2402.08117/) (2024)
- [Nearest Neighbor CCP-Based Molecular Sequence Analysis](https://mlsanity.com/paper/2409.04922/) (2024)
- [Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review](https://mlsanity.com/paper/2409.17516/) (2024)
- [Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks](https://mlsanity.com/paper/2410.12655/) (2024)
- [Sequence Analysis Using the Bezier Curve](https://mlsanity.com/paper/2503.14574/) (2025)
- [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)
- [Multi-Scale Reversible Chaos Game Representation: A Unified Framework for Sequence Classification](https://mlsanity.com/paper/2604.18477/) (2026)

## Abstract (arXiv)

Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between k-mers (sub-sequences of length k) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods -- they are designed in a "general" way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain.