A novel feature extraction method based on highly expressed SNPs for tissue-specific gene prediction

Abstract

Background Gene expression provides a means for an organism to produce gene products necessary for the organism to live. Variation in the significant gene expression levels can distinguish the gene and the tissue in which the gene is expressed. Tissue-specific gene expression, often determined by single nucleotide polymorphisms (SNPs), provides potential molecular markers or therapeutic targets for disease progression. Therefore, SNPs are good candidates for identifying disease progression. The current bioinformatics literature uses gene network modeling to summarize complex interactions between transcription factors, genes, and gene products. Here, our focus is on the SNPs’ impact on tissue-specific gene expression levels. To the best of our knowledge, we are not aware of any studies that distinguish tissue-specific genes using SNP expression levels. Method We propose a novel feature extraction method based on highly expressed SNPs using k-mers as features. We also propose optimal k-mer and feature sizes used in our approach. Determining the optimal sizes is still an open research question as it depends on the dataset and purpose of the analysis. Therefore, we evaluate our algorithm’s performance on a range of k-mer and feature sizes using a multinomial naive Bayes (MNB) classifier on genes in the 49 human tissues from the Genotype-Tissue Expression (GTEx) portal. Conclusions Our approach achieves practical performance results with k-mers of size 3. Based on the purpose of the analysis and the number of tissue-specific genes under study, feature sizes [7, 8, 9] and [8, 9, 10] are typically optimal for the machine learning model.

STR-based feature extraction and selection for genetic feature discovery in neurological disease genes

“…Determining the SNP and STR enrichment patterns has recently gained interest as potential genetic markers or therapeutic targets for disease progression 5 . In earlier work 6 , we discovered SNP genetic features by proposing a novel feature extraction algorithm based on SNP enrichment patterns from large-scale, real-world biological datasets (all 49 human tissues from the Genotype-Tissue Expression portal) for tissue-specific gene prediction. Our results demonstrated that variation in the SNP enrichment patterns could distinguish the gene and the tissue in which the gene is expressed.…”