iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm
- Authors
- Mahmoudi, Omid; Wahab, Abdul; Chong, Kil To
- Issue Date
- May-2020
- Publisher
- MDPI
- Keywords
- RNA N6-methyladenosine site; yeast genome; methylation; computational biology; deep learning; bioinformatics
- Citation
- GENES, v.11, no.5
- Journal Title
- GENES
- Volume
- 11
- Number
- 5
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81257
- DOI
- 10.3390/genes11050529
- ISSN
- 2073-4425
- Abstract
- One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 약학대학 > 약학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.