Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques
DOI:
https://doi.org/10.33448/rsd-v10i12.20561Keywords:
Machine Learning; Burkitt Lymphoma; MBL-2; Polymorphisms.Abstract
Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. Results: At first, an unsupervised classification was performed, which identified as two the number of groups that best represent the data present in our database, reaching 72.81% accuracy in the separation of patients and controls. Then, the supervised classification was performed, where the classifier obtained a 70.97% success rate, being possible to reach 75% accuracy in the best GridSearch configuration when performing a cross validation. Conclusion: It was not yet possible to conclude about the participation of the evaluated polymorphisms in the development of the BL, however the computational techniques used proved to be very promising for carrying out studies of this nature.
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Copyright (c) 2021 Jonathan Wagner de Medeiros; Anthony José da Cunha Carneiro Lins; Oluwarotimi Williams Samuel; Elker Lene Santos de Lima; Maria Luiza Tabosa de Carvalho Galvão; Bárbara Oliveira Silva; Giwellington Silva Albuquerque; Luísa Priscilla Oliveira de Lima; Maria Tereza Cartaxo Muniz
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