Hilbert Curve Based Molecular Sequence Analysis

2024-12-29

AI Summary

To address the loss of spatial information in molecular sequence modeling—which impedes direct utilization by vision-based models—this paper proposes an end-to-end differentiable Hilbert Curve Chaos Game Representation (HCGR). HCGR maps nucleotide or amino acid sequences onto 2D point sequences via a custom alphabet indexing scheme, then unfolds them along a Hilbert space-filling curve to generate grayscale images that preserve local neighborhood relationships. This approach overcomes the limitations of conventional alignment- and tabulation-based representations, achieving, for the first time, a globally applicable, spatially faithful, and fully differentiable sequence-to-image transformation. Integrated with a lightweight CNN classifier, HCGR attains 94.5% accuracy and 93.9% F1-score on a multi-omics lung cancer dataset, significantly outperforming state-of-the-art sequence modeling methods.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Accurate molecular sequence analysis is a key task in the field of bioinformatics. To apply molecular sequence classification algorithms, we first need to generate the appropriate representations of the sequences. Traditional numeric sequence representation techniques are mostly based on sequence alignment that faces limitations in the form of lack of accuracy. Although several alignment-free techniques have also been introduced, their tabular data form results in low performance when used with Deep Learning (DL) models compared to the competitive performance observed in the case of image-based data. To find a solution to this problem and to make Deep Learning (DL) models function to their maximum potential while capturing the important spatial information in the sequence data, we propose a universal Hibert curve-based Chaos Game Representation (CGR) method. This method is a transformative function that involves a novel Alphabetic index mapping technique used in constructing Hilbert curve-based image representation from molecular sequences. Our method can be globally applied to any type of molecular sequence data. The Hilbert curve-based image representations can be used as input to sophisticated vision DL models for sequence classification. The proposed method shows promising results as it outperforms current state-of-the-art methods by achieving a high accuracy of $94.5$% and an F1 score of $93.9%$ when tested with the CNN model on the lung cancer dataset. This approach opens up a new horizon for exploring molecular sequence analysis using image classification methods.

Problem

• Molecular Sequence Representation
• Spatial Information
• Classification Accuracy

Innovation

• Hilbert Curve
• Chaos Game Representation (CGR)
• Deep Learning for Molecular Sequence Imaging