Effective Classification of MicroRNA Precursors Using Feature Mining and AdaBoost Algorithms
作者： Ling Zhong , Jason T. L. Wang , Dongrong Wen , Virginie Aris , Patricia Soteropoulos
关键词:
摘要: MicroRNAs play important roles in most biological processes, including cell proliferation, tissue differentiation, and embryonic development, among others. They originate from precursor transcripts (pre-miRNAs), which contain phylogenetically conserved stem–loop structures. An bioinformatics problem is to distinguish the pre-miRNAs pseudo that have similar We present here a novel method for tackling this problem. Our method, named MirID, accepts an RNA sequence as input, classifies either positive (i.e., real pre-miRNA) or negative pre-miRNA). MirID employs feature mining algorithm finding combinations of features suitable building pre-miRNA classification models. These models are implemented using support vector machines, combined construct classifier ensemble. The accuracy ensemble further enhanced by utilization AdaBoost algorithm. When compared with two closely related tools on twelve species analyzed these tools, outperforms existing majority species. was also tested nine additional species, results showed high accuracies web server fully operational freely accessible at http://bioinformatics.njit.edu/MirID/. Potential applications software genomics medicine discussed.
sci-hub.se 参考文章(55)
David P Bartel, MicroRNAs: Genomics, Biogenesis, Mechanism, and Function Cell. ,vol. 116, pp. 281- 297 ,(2004) , 10.1016/S0092-8674(04)00045-5
Robert E. Schapire, A brief introduction to boosting international joint conference on artificial intelligence. ,vol. 2, pp. 1401- 1406 ,(1999)
Jianghui Liu, Jason TL Wang, Jun Hu, Bin Tian, A method for aligning RNA secondary structures and its application to RNA motif detection BMC Bioinformatics. ,vol. 6, pp. 89- 89 ,(2005) , 10.1186/1471-2105-6-89
Yun Zheng, Wynne Hsu, Mong Li Lee, Limsoon Wong, Exploring Essential Attributes for Detecting MicroRNA Precursors from Background Sequences Data Mining and Bioinformatics. pp. 131- 145 ,(2006) , 10.1007/11960669_12
Junilda Spirollari, Jason T.L. Wang, Kaizhong Zhang, Vivian Bellofatto, Yongkyu Park, Bruce A. Shapiro, Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization Bioinformatics and Biology Insights. ,vol. 3, pp. 51- 69 ,(2009) , 10.4137/BBI.S2578
Alain Sewer, Nicodème Paul, Pablo Landgraf, Alexei Aravin, Sébastien Pfeffer, Michael J Brownstein, Thomas Tuschl, Erik van Nimwegen, Mihaela Zavolan, Identification of clustered microRNAs using an ab initio prediction method BMC Bioinformatics. ,vol. 6, pp. 267- 267 ,(2005) , 10.1186/1471-2105-6-267
Chenghai Xue, Fei Li, Tao He, Guo-Ping Liu, Yanda Li, Xuegong Zhang, Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine BMC Bioinformatics. ,vol. 6, pp. 310- 310 ,(2005) , 10.1186/1471-2105-6-310
Lee P Lim, Margaret E Glasner, Soraya Yekta, Christopher B Burge, David P Bartel, Vertebrate microRNA genes. Science. ,vol. 299, pp. 1540- 1540 ,(2003) , 10.1126/SCIENCE.1080372
Matthew W. Anderson, Iris Schrijver, Next generation DNA sequencing and the future of genomic medicine. Genes. ,vol. 1, pp. 38- 69 ,(2010) , 10.3390/GENES1010038
E S Martens-Uzunova, S E Jalava, N F Dits, G J L H van Leenders, S Møller, J Trapman, C H Bangma, T Litman, T Visakorpi, G Jenster, Diagnostic and prognostic signatures from the small non-coding RNA transcriptome in prostate cancer Oncogene. ,vol. 31, pp. 978- 991 ,(2012) , 10.1038/ONC.2011.304