PhageVir: A Machine and Deep Learning Approach for Effective Prediction of Phage Virion Proteins

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dc.contributor.authorHUSSNAIN ARSHAD
dc.date.accessioned2025-08-29T09:33:09Z
dc.date.available2025-08-29T09:33:09Z
dc.date.issued2024
dc.description.abstractThis study uses machine and deep learning techniques to create an effective prediction model for PVPs. PVPs are key phage components that aid in the attachment and penetration of the phage to the host cell, allowing viral reproduction. PVP prediction accuracy can provide crucial insights into the molecular mechanisms of phage infection and may contribute to developing novel antibacterial treatments. Various state-of-the-art machine and deep learning approaches were used: including LGBM, RF, CNN1D, LSTM, RNN, GRU, and ANN to identify PVPs. Existing literature fervently supports the use of these algorithms based on recent bioinformatics studies, as they have proved helpful in processing sequential data. Precision metrics, like accuracy, sensitivity, specificity, and MCC, were calculated to assess the models' performance. Each model underwent independent testing, self-consistency, and cross-validation to achieve accurate findings. The results of this study demonstrate that machine and deep learning techniques accurately predict PVPs. The highest 10-fold cross-validation accuracy, sensitivity, specificity, and MCC score were achieved by CNN attaining 0.833 accuracy, 0.832 sensitivity, 0.834specificity, and an MCC score of 0.665. The ROC curve also revealed CNN performed well, achieving an AUC score of 0.927. Based on rigorous experimental evidence, it is inferred that the work proposes effective machine and deep learning techniques to classify PVPs accurately. The web server has been deployed at https://hussnain-arshad-phage virion.streamlit.app/
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/5729
dc.language.isoen
dc.publisherUMT, Lahore
dc.titlePhageVir: A Machine and Deep Learning Approach for Effective Prediction of Phage Virion Proteins
dc.typeThesis
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