GenEx: A Graph-Based Representational Paradigm for SARS-CoV-2 Variant Detection via Codon Co-occurrence Networks

• URL: http://arxiv.org/abs/2608.18238v1
• Date: Tue, 18 Aug 2026 18:24:45 GMT
• Title: GenEx: A Graph-Based Representational Paradigm for SARS-CoV-2 Variant Detection via Codon Co-occurrence Networks
• Abstract summary: GenEx is a pipeline that converts raw gene sequences into codon co-occurrence graphs and extracts more than 25 graph features.<n>We trained 23 benchmarked ML models against the latest SARS-CoV-2 variants, achieving remarkable results in detecting all SARS-CoV-2 variants.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics methods, including Sequence Alignment, Phylogenetic Analysis, and Mutation Frequency Statistics. These approaches use pairwise codon or nucleotide distance matrices to analyze gene sequences, treating them as linear strings rather than capturing their complex contextual interdependencies. We proposed GenEx, a pipeline that converts raw gene sequences into codon co-occurrence graphs and extracts more than 25 graph features. Our two most prominent techniques for graph generation and feature extraction are MSCG (Multi-Scale Codon Co-occurrence Graph) and LAPCG (Linear-time Adjacency PMI Codon Graph). Using these algorithms, we treated codon sequences as structured symbolic vocabularies interpretable to codon co-occurrence graph analysis, a representational paradigm borrowed from computational linguistics. Another major contribution includes implementing a spectral graph feature extraction using Singular Value Decomposition (SVD), using the squared singular value ($σ^2$) instead of the traditionally used eigenvalue, which helped us to amplify the separation between dominant and subdominant spectral components, thereby enhancing inter-class separability in downstream classification. And to further demonstrate that our method works, we trained 23 benchmarked ML models against the latest SARS-CoV-2 variants, achieving remarkable results in detecting all SARS-CoV-2 variants.

Related papers

• A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics [8.854289521774483]
  HESCAPE is a benchmark for evaluating cross-modal contrastive pretraining in spatial transcriptomics.<n>Gene models pretrained on spatial transcriptomics data outperform both those trained without spatial data and simple baseline approaches.<n>We identify batch effects as a key factor that interferes with effective cross-modal alignment.
  arXiv  Detail & Related papers  (2025-08-02T21:11:36Z)
• SpectralGap: Graph-Level Out-of-Distribution Detection via Laplacian Eigenvalue Gaps [19.580332929984028]
  We propose SpecGap, an effective post-hoc approach for OOD detection on graphs.<n> SpecGap achieves state-of-the-art performance across multiple benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-21T06:47:44Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• GCBLANE: A graph-enhanced convolutional BiLSTM attention network for improved transcription factor binding site prediction [0.0]
  GCBLANE is a graph-enhanced convolutional bidirectional Long Short-Term Memory (LSTM) attention network.<n>It integrates convolutional, multi-head attention, and recurrent layers with a graph neural network to detect key features for TFBS prediction.<n>On 690 ENCODE ChIP-Seq datasets, GCBLANE achieved an average AUC of 0.943, and on 165 ENCODE, it reached an AUC of 0.9495.
  arXiv  Detail & Related papers  (2025-03-16T06:52:03Z)
• ViralVectors: Compact and Scalable Alignment-free Virome Feature Generation [0.7874708385247353]
  The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. We propose emphsignatures, a compact vector generation from virome sequencing data that allows effective downstream analysis.
  arXiv  Detail & Related papers  (2023-04-06T06:46:17Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Using Signal Processing in Tandem With Adapted Mixture Models for Classifying Genomic Signals [16.119729980200955]
  We propose a novel technique that employs signal processing in tandem with Gaussian mixture models to improve the spectral representation of a sequence. Our method outperforms a similar state-of-the-art method on established benchmark datasets by an absolute margin of 6.06% accuracy.
  arXiv  Detail & Related papers  (2022-11-03T06:10:55Z)
• Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification [109.81283748940696]
  We introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show that some simulation-based approaches are more robust (and accurate) than others for specific embedding methods to certain adversarial attacks to the input sequences.
  arXiv  Detail & Related papers  (2022-07-18T19:16:56Z)
• Graph Spectral Embedding using the Geodesic Betweeness Centrality [76.27138343125985]
  We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure. GSE uses the solution of the Sylvester equation to capture both network structure and neighborhood proximity in a single representation.
  arXiv  Detail & Related papers  (2022-05-07T04:11:23Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• Embedding Graph Auto-Encoder for Graph Clustering [90.8576971748142]
  Graph auto-encoder (GAE) models are based on semi-supervised graph convolution networks (GCN) We design a specific GAE-based model for graph clustering to be consistent with the theory, namely Embedding Graph Auto-Encoder (EGAE) EGAE consists of one encoder and dual decoders.
  arXiv  Detail & Related papers  (2020-02-20T09:53:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.