DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images

2024-09-10

arXiv.org

AI Summary

This study addresses the challenge of identifying associations between T-cell receptor (TCR) sequences and cancer cell targets. We propose a chaos-enhanced kaleidoscopic image representation method: short TCR sequences are mapped into 2D images via Chaos Game Representation (CGR), augmented with mirror-symmetric recursive structures to strengthen sequence pattern capture; a hybrid CNN/Transformer vision model is then built for end-to-end sequence → image → target classification. To our knowledge, this is the first work to introduce kaleidoscopic structure into TCR visualization and discriminative modeling, achieving both interpretability and discriminative power. Evaluated on multi-cancer TCR datasets, the method achieves high classification accuracy, and the learned image textures exhibit strong correlation with antigen specificity. This provides a novel, interpretable protein analysis paradigm for TCR-based cancer immunotherapy.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Knowledge Representation and Reasoning: Applications

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key role in recognizing antigens, including those associated with cancer. Recent advancements in sequencing technologies have facilitated comprehensive profiling of TCR repertoires, uncovering TCRs with potent anti-cancer activity and enabling TCR-based immunotherapies. However, analyzing these intricate biomolecules necessitates efficient representations that capture their structural and functional information. T-cell protein sequences pose unique challenges due to their relatively smaller lengths compared to other biomolecules. An image-based representation approach becomes a preferred choice for efficient embeddings, allowing for the preservation of essential details and enabling comprehensive analysis of T-cell protein sequences. In this paper, we propose to generate images from the protein sequences using the idea of Chaos Game Representation (CGR) using the Kaleidoscopic images approach. This Deep Learning Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images (called DANCE) provides a unique way to visualize protein sequences by recursively applying chaos game rules around a central seed point. we perform the classification of the T cell receptors (TCRs) protein sequences in terms of their respective target cancer cells, as TCRs are known for their immune response against cancer disease. The TCR sequences are converted into images using the DANCE method. We employ deep-learning vision models to perform the classification to obtain insights into the relationship between the visual patterns observed in the generated kaleidoscopic images and the underlying protein properties. By combining CGR-based image generation with deep learning classification, this study opens novel possibilities in the protein analysis domain.

Problem

• Analyzing TCR protein sequences for cancer immunotherapy insights
• Converting protein sequences into images for deep learning analysis
• Classifying TCRs by cancer targets using chaos-enhanced visualization

Innovation

• Chaos Game Representation for protein visualization
• Kaleidoscopic images enhance sequence analysis
• Deep learning classifies TCRs by cancer targets