Back to Search
Start Over
Tissue-specific RNA methylation prediction from gene expression data using sparse regression models.
- Source :
-
Computers in biology and medicine [Comput Biol Med] 2024 Feb; Vol. 169, pp. 107892. Date of Electronic Publication: 2023 Dec 26. - Publication Year :
- 2024
-
Abstract
- N6-methyladenosine (m <superscript>6</superscript> A) is a highly prevalent and conserved post-transcriptional modification observed in mRNA and long non-coding RNA (lncRNA). Identifying potential m <superscript>6</superscript> A sites within RNA sequences is crucial for unraveling the potential influence of the epitranscriptome on biological processes. In this study, we introduce Exp2RM, a novel approach that formulates single-site-based tissue-specific elastic net models for predicting tissue-specific methylation levels utilizing gene expression data. The resulting ensemble model demonstrates robust predictive performance for tissue-specific methylation levels, with an average R-squared value of 0.496 and a median R-squared value of 0.482 across all 22 human tissues. Since methylation distribution varies among tissues, we trained the model to incorporate similar patterns, significantly improves accuracy with the median R-squared value increasing to 0.728. Additonally, functional analysis reveals Exp2RM's ability to capture coefficient genes in relevant biological processes. This study emphasizes the importance of tissue-specific methylation distribution in enhancing prediction accuracy and provides insights into the functional implications of methylation sites.<br />Competing Interests: Declaration of competing interest The authors declare no competing interests.<br /> (Copyright © 2023 Elsevier Ltd. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1879-0534
- Volume :
- 169
- Database :
- MEDLINE
- Journal :
- Computers in biology and medicine
- Publication Type :
- Academic Journal
- Accession number :
- 38171264
- Full Text :
- https://doi.org/10.1016/j.compbiomed.2023.107892