BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species.
MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 12 |
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| Autores principales: | , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/22/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117597215&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117597215 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/22/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117597215 117597215 117597215 10.1155/2016/9565689 117597215 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species. aug: au: Jiang, Limin Zhang, Jingjun Xuan, Ping Zou, Quan affil: School of Computer Science and Technology, Tianjin University, Tianjin 300350, China sug: subj: RNA Classification RNA Analysis Neural Networks (Computer) Descriptive Statistics Precision Validity Data Analysis Software Algorithms Correlation Coefficient Sensitivity and Specificity Plants Insects Reptiles Epstein-Barr Virus Frogs and Toads Funding Source ab: MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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