iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens
2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to...
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| Published in | Journal of computational biology Vol. 25; no. 11; p. 1266 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
01.11.2018
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| Subjects | |
| Online Access | Get more information |
| ISSN | 1557-8666 |
| DOI | 10.1089/cmb.2018.0004 |
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| Abstract | 2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2'-O-methylation sites. In this study, based on the experimental 2'-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%. |
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| AbstractList | 2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2'-O-methylation sites. In this study, based on the experimental 2'-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%. |
| Author | Chen, Wei Lv, Hao Lin, Hao Yang, Hui Ding, Hui |
| Author_xml | – sequence: 1 givenname: Hui surname: Yang fullname: Yang, Hui organization: 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China – sequence: 2 givenname: Hao surname: Lv fullname: Lv, Hao organization: 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China – sequence: 3 givenname: Hui surname: Ding fullname: Ding, Hui organization: 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China – sequence: 4 givenname: Wei surname: Chen fullname: Chen, Wei organization: 2 Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology , Tangshan, China – sequence: 5 givenname: Hao surname: Lin fullname: Lin, Hao organization: 1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30113871$$D View this record in MEDLINE/PubMed |
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| Keywords | chemical property Homo sapiens PseKNC RNA sequence 2′-O-methylation |
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| Title | iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens |
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