Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection
文献类型：期刊论文
| 作者 | Chen, Lei1,2; Wang, ShaoPeng1; Cai, Yu-Dong1; Zhang, Yu-Hang3; Huang, Tao3; Huang, Guohua4; Pan, Xiaoyong5; , |
| 刊名 | MOLECULAR GENETICS AND GENOMICS
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| 出版日期 | 2018 |
| 卷号 | 293期号:1页码:137-149 |
| 关键词 | cirRNAs lncRNAs Minimum redundancy maximum relevance Hierarchical extreme learning machine algorithm |
| ISSN号 | 1617-4615 |
| DOI | 10.1007/s00438-017-1372-7 |
| 文献子类 | Article |
| 英文摘要 | As non-coding RNAs, circular RNAs (cirRNAs) and long non-coding RNAs (lncRNAs) have attracted an increasing amount of attention. They have been confirmed to participate in many biological processes, including playing roles in transcriptional regulation, regulating protein-coding genes, and binding to RNA-associated proteins. Until now, the differences between these two types of non-coding RNAs have not been fully uncovered. It is still quite difficult to detect cirRNAs from other lncRNAs using simple techniques. In this study, we investigated these two types of non-coding RNAs using several computational methods. The purpose was to extract important factors that could distinguish cirRNAs from other lncRNAs and build an effective classification model to distinguish them. First, we collected cirRNAs, lncRNAs and their representations from a previous study, in which each cirRNA or lncRNA was represented by 188 features derived from its graph representation, sequence and conservation properties. Second, these features were analyzed by the minimum redundancy maximum relevance (mRMR) method. The obtained mRMR feature list, incremental feature selection method and hierarchical extreme learning machine algorithm were employed to build an optimal classification model with sensitivity of 0.703, specificity of 0.850, accuracy of 0.789 and a Matthews correlation coefficient of 0.561. Finally, we analyzed the 16 most important features. Of them, the sequences and structures of the RNA molecule were top ranking, implying they can be potential indicators of differences between cirRNAs and other lncRNAs. Meanwhile, other features of evolutionary conversation, sequence consecution were also important. |
| 学科主题 | Biochemistry & Molecular Biology ; Genetics & Heredity |
| WOS关键词 | LONG NONCODING RNAS ; SUPPORT VECTOR MACHINE ; OPEN READING FRAME ; TRANSPOSABLE ELEMENTS ; CIRCULAR RNAS ; GENE-EXPRESSION ; BINDING PROTEINS ; MOLECULAR FRAGMENTS ; MINIMUM REDUNDANCY ; MAXIMUM RELEVANCE |
| 语种 | 英语 |
| WOS记录号 | WOS:000423833400012 |
| 出版者 | SPRINGER HEIDELBERG |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/1021]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Shanghai Univ, Coll Life Sci, Shanghai 200444, Peoples R China; 2.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China; 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 4.Shaoyang Univ, Dept Math, Shaoyang 422000, Hunan, Peoples R China; 5.Erasmus MC, Dept Med Informat, Rotterdam, Netherlands, |
| 推荐引用方式 GB/T 7714 | Chen, Lei,Wang, ShaoPeng,Cai, Yu-Dong,et al. Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection[J]. MOLECULAR GENETICS AND GENOMICS,2018,293(1):137-149. |
| APA | Chen, Lei.,Wang, ShaoPeng.,Cai, Yu-Dong.,Zhang, Yu-Hang.,Huang, Tao.,...&,.(2018).Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.MOLECULAR GENETICS AND GENOMICS,293(1),137-149. |
| MLA | Chen, Lei,et al."Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection".MOLECULAR GENETICS AND GENOMICS 293.1(2018):137-149. |
入库方式： OAI收割
来源：上海营养与健康研究所
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