The Human Leukocyte Antigen (HLA) gene system situated on Chromosome 6 has been the subject of extensive research, primarily due to its vital role in transplantation and its links to autoimmune, infectious, and inflammatory diseases. The classical HLA genes, including HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, and HLA-DRB1, exhibit a high degree of polymorphism among individuals within a population. As many changes in the allele, computational imputation-based HLA typing is used extensively and in machine learning, it is possible through supervised learning. There are many methods available for doing HLA imputation from HLA and SNP genotype data using different methods and algorithms. The present study carefully examined the research articles and noticed that the Ensemble methods, Random Forest and Boosting algorithms are the few effective methods for HLA imputation. Attribute bagging is a technique that enhances the accuracy and stability of classifier ensembles by employing bootstrap aggregating and random variable selection. The ensemble classifier method involves two main phases. In the first phase, a collection of base-level classifiers is generated, and in the second phase, a metalevel classifier is trained to combine the outputs of the base-level classifiers. The R statistical programming language is utilized by Bioconductor software packages such as HIBAG, which are designed for the research community to impute (assign) HLA types using SNP data. In the present study, the details of different methods, software and algorithms used for HLA imputation are discussed for the non-biologists and biologists who work on HLA allele type prediction.
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