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Arxiv
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139
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DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images
- Cancer is a complex disease involving uncontrolled cell growth, with T cell receptors (TCRs) playing a crucial role in recognizing antigens, including those related to cancer.
- Advancements in sequencing technologies have allowed for detailed profiling of TCR repertoires, leading to the discovery of potent anti-cancer TCRs and the development of TCR-based immunotherapies.
- Analyzing T-cell protein sequences presents challenges due to their shorter lengths, necessitating efficient representations.
- A proposed solution involves generating chaos-enhanced kaleidoscopic images from protein sequences using Chaos Game Representation (CGR).
- The Deep Learning Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images (DANCE) method enables visualization of protein sequences by applying chaos game rules around a central point.
- The DANCE method is utilized to classify TCR protein sequences associated with specific cancer cells, leveraging the immune response of TCRs against cancer.
- TCR sequences are transformed into images via the DANCE method, and deep-learning vision models are employed for classification, linking visual patterns in the images with underlying protein properties.
- By combining CGR-based image generation with deep learning classification, this study introduces new possibilities in protein analysis.
- The research project focuses on improving analysis techniques for T-cell protein sequences, particularly in the context of cancer immunity.
- The DANCE method provides a unique visual representation of protein sequences, aiding in the exploration of TCR properties and their interactions with cancer cells.
- The study highlights the significance of innovative approaches, such as chaos-enhanced kaleidoscopic images, in enhancing protein sequence analysis and classification.
- Efficient representation of TCR sequences through image-based approaches allows for detailed analysis and classification using deep learning methods.
- The integration of Chaos Game Representation and deep learning techniques offers a promising avenue for studying the relationship between visual patterns and protein properties.
- TCR-based immunotherapies may benefit from the insights gained through the DANCE method's classification of TCR protein sequences.
- The proposed methodology showcases the potential of combining visual data representation with advanced analytical tools in protein sequence analysis.
- In conclusion, the DANCE approach using chaos-enhanced kaleidoscopic images presents a novel and effective strategy for analyzing and classifying T-cell protein sequences with implications for cancer research and immunotherapy development.
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