ArticlePloS one2019
Characterization of bovine (Bos taurus) imprinted genes from genomic to amino acid attributes by data mining approaches.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis.Botanical studies · 2024Article
- Differential Expression of RNAseq Imprinted Genes from Bovine Females Before and After Puberty.Biochemical genetics · 2023Article
- Defining Candidate Imprinted loci inGenes · 2023Article
- Modification of Experimental Design and Statistical Method for Mapping Imprinted QTLs Based on Immortalized FFrontiers in genetics · 2020Article
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Authors and funding
5 authors.
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Abstract
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.
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