Open AccessCharacterization of bovine (Bos taurus) imprinted genes from genomic to amino acid attributes by data mining approaches
Author(s) -
Keyvan Karami,
Saeed Zerehdaran,
Ali Javadmanesh,
Mohammad Mahdi Shariati,
Hossein Fallahi
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0217813
Subject(s) - genomic imprinting , genome , gene , biology , bovine genome , weighting , genomics , cluster analysis , computational biology , genetics , feature selection , artificial intelligence , computer science , gene expression , medicine , radiology , dna methylation
Genomic imprinting results in monoallelic expression of genes in mammals and flowering plants. Understanding the function of imprinted genes improves our knowledge of the regulatory processes in the genome. In this study, we have employed classification and clustering algorithms with attribute weighting to specify the unique attributes of both imprinted (monoallelic) and biallelic expressed genes. We have obtained characteristics of 22 known monoallelically expressed (imprinted) and 8 biallelic expressed genes that have been experimentally validated alongside 208 randomly selected genes in bovine ( Bos taurus ). Attribute weighting methods and various supervised and unsupervised algorithms in machine learning were applied. Unique characteristics were discovered and used to distinguish mono and biallelic expressed genes from each other in bovine. To obtain the accuracy of classification, 10-fold cross-validation with concerning each combination of attribute weighting (feature selection) and machine learning algorithms, was used. Our approach was able to accurately predict mono and biallelic genes using the genomics and proteomics attributes.