# PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences arXiv: https://arxiv.org/abs/2201.02273 ยท HTML: https://mlsanity.com/paper/2201.02273/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 11 of its 85 references that are in the map. - First posted on arXiv 6 Jan 2022. ## Builds on (most cited first, 11 of 11) - [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) - [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) - [Efficient Analysis of COVID-19 Clinical Data using Machine Learning Models](https://mlsanity.com/paper/2110.09606/) (2021) ## Built on by (most cited first, 22 of 22) - [Efficient Approximate Kernel Based Spike Sequence Classification](https://mlsanity.com/paper/2209.04952/) (2022) - [Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data](https://mlsanity.com/paper/2211.08267/) (2022) - [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) - [Seq-HyGAN: Sequence Classification via Hypergraph Attention Network](https://mlsanity.com/paper/2303.02393/) (2023) - [DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images](https://mlsanity.com/paper/2409.06694/) (2024) - [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) - [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) - [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-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) - [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) COVID-19 pandemic, is still unknown and is an important open question. There are speculations that bats are a possible origin. Likewise, there are many closely related (corona-) viruses, such as SARS, which was found to be transmitted through civets. The study of the different hosts which can be potential carriers and transmitters of deadly viruses to humans is crucial to understanding, mitigating and preventing current and future pandemics. In coronaviruses, the surface (S) protein, or spike protein, is an important part of determining host specificity since it is the point of contact between the virus and the host cell membrane. In this paper, we classify the hosts of over five thousand coronaviruses from their spike protein sequences, segregating them into clusters of distinct hosts among avians, bats, camels, swines, humans and weasels, to name a few. We propose a feature embedding based on the well-known position-weight matrix (PWM), which we call PWM2Vec, and use to generate feature vectors from the spike protein sequences of these coronaviruses. While our embedding is inspired by the success of PWMs in biological applications such as determining protein function, or identifying transcription factor binding sites, we are the first (to the best of our knowledge) to use PWMs in the context of host classification from viral sequences to generate a fixed-length feature vector representation. The results on the real world data show that in using PWM2Vec, we are able to perform comparably well as compared to baseline models. We also measure the importance of different amino acids using information gain to show the amino acids which are important for predicting the host of a given coronavirus.