ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis

[2508.07345] ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2508.07345 (cs) [Submitted on 10 Aug 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis Authors: Samiha Afaf Neha , Md. Ishrak Khan , Abir Ahammed Bhuiyan View a PDF of the paper titled ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis, by Samiha Afaf Neha and 2 other authors View PDF HTML (experimental) Abstract: \textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine learning, have emerged for annotating phage protein sequences from high-throughput sequencing. However, effective annotation requires specialized sequence encodings. Our paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing techniques, yielding competitive performance in PVP classification using pre-trained convolutional neural networks. Additionally, our study evaluates prediction uncertainty in binary PVP classification through Monte Carlo Dropout (MCD). \textbf{Methods:} ProteoKnight adapts the classical DNA-Walk algorithm for protein sequences, incorporating pixel colors and adjusting walk distances to capture intricate protein features. Encoded sequences were classified using multiple pre-trained CNNs. Variance and entropy measures assessed prediction uncertainty across proteins of various classes and lengths. \textbf{Results:} Our experiments achieved 90.8% accuracy in binary classification, comparable to state-of-the-art methods. Multi-class classification accuracy remains suboptimal. Our uncertainty analysis unveils variability in prediction confidence influenced by protein class and sequence length. \textbf{Conclusions:} Our study surpasses frequency chaos game representation (FCGR) by introducing novel image encoding that mitigates spatial information loss limitations. Our classification technique yields accurate and robust PVP predictions while identifying low-confidence predictions. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2508.07345 [cs.LG] (or arXiv:2508.07345v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2508.07345 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Samiha Afaf Neha [ view email ] [v1] Sun, 10 Aug 2025 13:45:08 UTC (22,463 KB) [v2] Sat, 15 Aug 2026 09:20:39 UTC (21,586 KB) Full-text links: Access Paper: View a PDF of the paper titled ProteoKnight: Convolution-based Phage Virion Protein Classification and Uncertainty Analysis, by Samiha Afaf Neha and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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