iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm
DC Field | Value | Language |
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dc.contributor.author | Mahmoudi, Omid | - |
dc.contributor.author | Wahab, Abdul | - |
dc.contributor.author | Chong, Kil To | - |
dc.date.accessioned | 2021-06-11T06:40:32Z | - |
dc.date.available | 2021-06-11T06:40:32Z | - |
dc.date.created | 2021-06-11 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 2073-4425 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81257 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | GENES | - |
dc.title | iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000542276700023 | - |
dc.identifier.doi | 10.3390/genes11050529 | - |
dc.identifier.bibliographicCitation | GENES, v.11, no.5 | - |
dc.description.isOpenAccess | N | - |
dc.citation.title | GENES | - |
dc.citation.volume | 11 | - |
dc.citation.number | 5 | - |
dc.contributor.affiliatedAuthor | Mahmoudi, Omid | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | RNA N6-methyladenosine site | - |
dc.subject.keywordAuthor | yeast genome | - |
dc.subject.keywordAuthor | methylation | - |
dc.subject.keywordAuthor | computational biology | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | bioinformatics | - |
dc.subject.keywordPlus | AMINO-ACID-COMPOSITION | - |
dc.subject.keywordPlus | MESSENGER-RNA | - |
dc.subject.keywordPlus | N-6-METHYLADENOSINE SITES | - |
dc.subject.keywordPlus | SUSCEPTIBILITY VARIANTS | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | BREAST | - |
dc.subject.keywordPlus | ASSOCIATION | - |
dc.subject.keywordPlus | REVEALS | - |
dc.subject.keywordPlus | PROGRAM | - |
dc.subject.keywordPlus | CANCER | - |
dc.relation.journalResearchArea | Genetics & Heredity | - |
dc.relation.journalWebOfScienceCategory | Genetics & Heredity | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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