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Sharma G, Rana PS, Bawa S. Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus. IEEE/ACM Trans Comput Biol Bioinform. 2021;18(4):1524-1534doi: 10.1109/TCBB.2019.2944610.
Sharma, G., Rana, P. S., & Bawa, S. (2021). Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus. IEEE/ACM transactions on computational biology and bioinformatics, 18(4), 1524-1534. https://doi.org/10.1109/TCBB.2019.2944610
Sharma, Gaurav, et al. "Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus." IEEE/ACM transactions on computational biology and bioinformatics vol. 18,4 (2021): 1524-1534. doi: https://doi.org/10.1109/TCBB.2019.2944610
Sharma G, Rana PS, Bawa S. Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus. IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1524-1534. doi: 10.1109/TCBB.2019.2944610. Epub 2021 Aug 06. PMID: 31567100.
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IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1524-1534. doi: 10.1109/TCBB.2019.2944610. Epub 2021 Aug 06.

Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus.

IEEE/ACM transactions on computational biology and bioinformatics

Gaurav Sharma, Prashant Singh Rana, Seema Bawa

PMID: 31567100 DOI: 10.1109/TCBB.2019.2944610

Abstract

Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is most prominent in scourging over 20 million people around the world and still not much effort has been made in understanding the epidemiology and controlling the prevalence of this virus. This condition further worsens when most of the infected cases remain asymptomatic throughout their lifetime due to the limited diagnostic methods; that are most of the times unavailable for timely detection of infected individuals. Moreover, at present, there is no licensed vaccination for HTLV-1 infection. Therefore, there is a need to develop the faster and efficient diagnostic method for the detection of HTLV-1. Influenced from the outcomes of the machine learning techniques in the field of bio-informatics, this is the first study in which 64 hybrid machine learning techniques have been proposed for the prediction of different type of HTLVs (HTLV-1, HTLV-2, and HTLV-3). The hybrid techniques are built by permutation and combination of four classification methods, four feature weighting, and four feature selection techniques. The proposed hybrid models when evaluated on the basis of various model evaluation parameters are found to be capable of efficiently predicting the type of HTLVs. The best hybrid model has been identified by having accuracy, an AUROC value, and F1 score of 99.85 percent, 0.99, and 0.99, respectively. This kind of the system can assist the current diagnostic system for the detection of HTLV-1 as after the molecular diagnostics of HTLV by various screening tests like enzyme-linked immunoassay or particle agglutination assays there is always a need of confirmatory tests like western blotting, immuno-fluorescence assay, or radio-immuno-precipitation assay for distinguishing HTLV-1 from HTLV-2. These confirmatory tests are indeed very complex analytical techniques involving various steps. The proposed hybrid techniques can be used to support and verify the results of confirmatory test from the protein mixture. Furthermore, better insights about the virus can be obtained by exploring the physicochemical properties of the protein sequences of HTLVs.

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