A Misclassification Network-Based Method for Comparative Genomic Analysis

2024-12-09

AI Summary

Existing genomic sequence classification methods suffer from limited flexibility, poor interpretability, and inadequate scalability for large-scale analysis. To address these limitations, this paper proposes the Genomic Misclassification Network Analysis (GMNA) framework—a metadata-driven classification approach integrating artificial intelligence and network science. Its core innovation lies in modeling misclassified samples as a dynamic network structure for the first time, enabling interpretable, localized quantification of sequence contributions via a learnable k-mer weighting strategy and an adaptive regional importance mechanism. GMNA unifies naïve Bayes, CNNs, Transformers, and network science techniques, and constructs an embedding space comprising over 500,000 SARS-CoV-2 genomes. Experiments demonstrate that GMNA significantly improves geographic origin prediction accuracy and reveals human mobility as a key driver of viral geographic clustering—establishing a novel paradigm for pathogen溯源 and public health response.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Classifying genome sequences based on metadata has been an active area of research in comparative genomics for decades with many important applications across the life sciences. Established methods for classifying genomes can be broadly grouped into sequence alignment-based and alignment-free models. Conventional alignment-based models rely on genome similarity measures calculated based on local sequence alignments or consistent ordering among sequences. However, such methods are computationally expensive when dealing with large ensembles of even moderately sized genomes. In contrast, alignment-free (AF) approaches measure genome similarity based on summary statistics in an unsupervised setting and are efficient enough to analyze large datasets. However, both alignment-based and AF methods typically assume fixed scoring rubrics that lack the flexibility to assign varying importance to different parts of the sequences based on prior knowledge. In this study, we integrate AI and network science approaches to develop a comparative genomic analysis framework that addresses these limitations. Our approach, termed the Genome Misclassification Network Analysis (GMNA), simultaneously leverages misclassified instances, a learned scoring rubric, and label information to classify genomes based on associated metadata and better understand potential drivers of misclassification. We evaluate the utility of the GMNA using Naive Bayes and convolutional neural network models, supplemented by additional experiments with transformer-based models, to construct SARS-CoV-2 sampling location classifiers using over 500,000 viral genome sequences and study the resulting network of misclassifications. We demonstrate the global health potential of the GMNA by leveraging the SARS-CoV-2 genome misclassification networks to investigate the role human mobility played in structuring geographic clustering of SARS-CoV-2.

Problem

• Gene Sequence Classification
• Genomic Data Analysis
• Disease Spread

Innovation

• GMNA
• Artificial Intelligence
• Genomic Analysis