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DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images by Murad, Taslim; Chourasia, Prakash; Ali, Sarwan; Khan, Imdad Ullah; Patterson, Murray is a scholarly article available to read on EtoBox.

What is DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images about?

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 p

Author

Murad, Taslim; Chourasia, Prakash; Ali, Sarwan; Khan, Imdad Ullah; Patterson, Murray

Published

2024

Language

EN