IITM LOGO V17 FILE 1

IITM Journal of Information Technology

ISSN (P) 2395-5457 | Single Blind Peer Reviewed Journal

Published By
INSTITUTE OF INNOVATION IN TECHNOLOGY & MANAGEMENT
Affiliated to GGSIPU, NAAC Grade ‘A’, ISO 14001:2015, 17020:2012, 21001:2018 & 50001:2018 Certified,
A Grade by GNCTD, A++ Grade by SFRC

Enhancing HIV Protease Cleavage Site Identification: A Comparative Analysis of F1 Score, NPV, and MCC

Navneet Kaur
Author

Keywords: F1 Score, MCC, Negative, Predictive Value.

Abstract: HIV and its most severe manifestation, AIDS, continue to be a major global health concern. Understanding the proteolytic activities of HIV's protease enzyme is essential for developing effective strategies to combat viral progression and transmission. Although researchers have previously created antiretroviral therapies and inhibitors, issues with toxicity and limited availability persist. This paper examines the current state of predictive models designed to identify protease cleavage sites in HIV-I AIDS proteins, offering an overview of the employed methodologies, data, and existing challenges. By reviewing published works and methodologies to date, this paper aims to provide insights into the present capabilities of machine learning models, specifically DCNN, and potential future advancements in predicting protease cleavage sites for HIV-I AIDS. Additionally, we propose a novel approach that integrates feature extraction and classification using machine learning techniques. The research objective is to conduct a comprehensive analysis of confusion matrix performance metrics, including NPV, F1 Score, and MCC, which are utilized to evaluate machine learning model performance in binary classification tasks.

References:

  1. Briand, L. C., Daly, J., and Wüst, J., "A unified framework for coupling measurement in object oriented systems", IEEE Transactions on Software Engineering, 25, 1, January 1999, pp. 91-121.
  2. Maletic, J. I., Collard, M. L., and Marcus, A., "Source Code Files as Structured Documents", in Proceedings 10th IEEE International Workshop[1] Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2021). Inception recurrent convolutional neural network for object recognition. Machine Vision and Applications, 32(1). https://doi.org/10.1007/s00138-020-01157-3
  3. Buvé, A., Bishikwabo-Nsarhaza, K., & Mutangadura, G. (2002). The spread and effect of HIV-1 infection in sub-Saharan Africa. The Lancet, 359(9322), 2011-2017. https://doi.org/10.1016/s0140-6736(02)08823-2
  4. Ghosh, A. K., Osswald, H. L., & Prato, G. (2016). Recent Progress in the Development of HIV-1 Protease Inhibitors for the Treatment of HIV/AIDS. Journal of Medicinal Chemistry, 59(11), 5172-5208. https://doi.org/10.1021/acs.jmedchem.5b01697
  5. Gilbert, M. T. P., Rambaut, A., Spira, T. J., Pitchenik, A. E., Worobey, M., & Wlasiuk, G. (2007). The emergence of HIV/AIDS in the Americas and beyond. Proceedings of the National Academy of Sciences, 104(47), 18566-18570.https://doi.org/10.1073/pnas.0705329104
  6. Gulnik, S., Erickson, J. W., & Xie, D. (2000). HIV protease: Enzyme function and drug resistance. Vitamins and Hormones, 58,213-256.https://doi.org/10.1016/s0083-6729(00)58026-1
  7. Hu, W., Li, H., Zhang, F., Wei, L., & Huang, Y. (2015). Deep Convolutional Neural Networks for Hyperspectral Image Classification. Journal of Sensors, 2015(2015), 1-12. https://doi.org/10.1155/2015/258619
  8. Khalil, A., El-Shafai, W., Abd El-Samie, F. E., Rihan, M., Mahrous, Y., Dessouky, M. I., El-Rabaie, E. M., Soltan, E., El-Banby, G. M., Elsherbeeny, Z., Saleeb, A. A., El-Bendary, M. A. M., El-Fishawy, A. S., Khalaf, A. A. M., E Ibrahim, F., Haggag, N., Messiha, N. W., Algarni, A. D., Soliman, N. F., ... El-Dokany, I. (2021). Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems. International Journal for Numerical Methods in Biomedical Engineering, 38(1). https://doi.org/10.1002/cnm.3530
  9. Li, J., Du, Q., Li, Y., Xi, B., Zhao, X., & Hu, J. (2018). Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network. IEEE Geoscience and Remote Sensing Letters, 15(2), 292-296. https://doi.org/10.1109/lgrs.2017.2786272
  10. Nguyen, K., Fookes, C., & Sridharan, S. (2015). Improving deep convolutional neural networks with unsupervised feature learning. 11,2270-2274. https://doi.org/10.1109/icip.2015.7351206
  11. Norris, P. J., & Rosenberg, E. S. (2002). CD4(+) T helper cells and the role they play in viral control. Journal of Molecular Medicine (Berlin, Germany), 80(7), 397-405. https://doi.org/10.1007/s00109-002-0337-3
  12. You, L., Garwicz, D., & RöGnvaldsson, T. (2005). Comprehensive bioinformatic analysis of the specificity of human immunodeficiency virus type 1 protease. Journal of Virology, 79(19), 12477-12486. https://doi.org/10.1128/jvi.79.19.12477-12486.2005
  13. Zhou, Y., Xu, F., Jin, Y.-Q., & Wang, H. (2016). Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks. IEEE Geoscience and Remote Sensing Letters, 13(12), 1935-1939. https://doi.org/10.1109/lgrs.2016.2618840
  14. Palmal, S., Saha, S., & Tripathy, S. (2023). Integrating Multi-view Feature Extraction and Fuzzy Rank-Based Ensemble for Accurate HIV-1 Protease Cleavage Site Prediction (pp. 480-492). Springer Science+Business Media. https://doi.org/10.1007/978-981-99-8141-0 36
  15. Hu, L., Li, Z., Tan, Z., Zhao, C., & Zhou, X. (2022). Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach. BMC Bioinformatics, 23(1). https://doi.org/10.1186/s12859-022-04999-y
  16. Onah, E. I., Uzor, P. F., Ugwoke, I. C., Eze, J. U., Ugwuanyi, S. T., Chukwudi, I. R., & Ibezim, A. (2022). Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors. BMC Bioinformatics, 23(1). https://doi.org/10.1186/s12859-022-05017-x
  17. Kaur, N., & Ghai, W. (2021). Performance Analysis of Deep CNN Assisted Optimized HIV-I Protease Cleavage Site Prediction with Hybridized Technique (pp. 529-540). Springer, Singapore. https://doi.org/10.1007/978-981-33-4909-4_40
  18. Li, Z., Hu, L., Tang, Z., & Zhao, C. (2021). Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning. Frontiers in Genetics, 12(3), 658078. https://doi.org/10.3389/FGENE.2021.658078
  19. ]Hu, L., Hu, P., Luo, X., Yuan, X., & You, Z.-H. (2020). Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 2017-2028. https://doi.org/10.1109/TCBB.2019.2914208, Prediction of HIV-1 Protease Cleavage Site from Octapeptide Sequence Information using Selected Classifiers and Hybrid Descriptors. (2022). https://doi.org/10.21203/rs.3.rs-1688464/v1
  20. Lu, X., Wang, L., & Jiang, Z. (2018). The Application of Deep Learning in the Prediction of HIV-1 Protease Cleavage Site. International Conference on Systems, 1299-1304. https://doi.org/10.1109/ICSAI.2018.8599496
  21. Singh, D., Singh, P. K., & Sisodia, D. (2019). Evolutionary based ensemble framework for realizing transfer learning in HIV-1 Protease cleavage sites prediction. Applied Intelligence, 49(4), 1260-1282. https://doi.org/10.1007/S10489-018-1323-Y
Scroll to Top