[1]张 明,徐 妍,陈 韬,等.基于核酸物化属性显著性约简的m6A位点识别[J].南京理工大学学报(自然科学版),2019,43(02):199.[doi:10.14177/j.cnki.32-1397n.2019.43.02.012]
 Zhang Ming,Xu Yan,Chen Tao,et al.Identification of m6A site based on reduction ofnucleotide physical-chemical properties significance[J].Journal of Nanjing University of Science and Technology,2019,43(02):199.[doi:10.14177/j.cnki.32-1397n.2019.43.02.012]
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基于核酸物化属性显著性约简的m6A位点识别()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
43卷
期数:
2019年02期
页码:
199
栏目:
出版日期:
2019-04-26

文章信息/Info

Title:
Identification of m6A site based on reduction ofnucleotide physical-chemical properties significance
文章编号:
1005-9830(2019)02-0199-10
作者:
张 明12徐 妍1陈 韬1王长宝1於东军2
1.江苏科技大学 计算机学院,江苏 镇江 212003; 2.南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Zhang Ming12Xu Yan1Chen Tao1Wang Changbao1Yu Dongjun2
1.School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.School of Computer Science and Engineering,Nanjing University of Scienceand Technology,Nanjing 210094,China
关键词:
启发式约简算法 分类器融合 伪二核苷酸成分 自协方差与互协方差
Keywords:
heuristic reduction algorithm classifier ensemble pseudo dinucleotide composition auto variance and cross covariance
分类号:
TP18
DOI:
10.14177/j.cnki.32-1397n.2019.43.02.012
摘要:
为了提高核糖核酸(RNA)序列上6-甲基腺嘌呤(m6A)位点的预测精度,该文提出一种新的基于核酸物化属性显著性度量的K重选择启发式约简算法通过该算法获得K个物化属性约简子集来重新编码RNA样本,并结合支持向量机(SVM)训练得到K个基分类器,再通过分类器融合方法构建了m6A位点预测器。最后,在相同的基准数据上,采用留一法交叉验证分别验证了伪二核苷酸成分分析和自协方差与互协方差变换两种典型的基于核酸物化属性的特征表示算法。实验结果表明,该文算法可显著提高m6A位点预测的总体性能。
Abstract:
In order to improve the prediction accuracy of N6-methyladenosine(m6A)sites on the ribonucleic acid(RNA)sequence,a novel K-fold selection heuristic reduction algorithm is developed here based on the significance measurement of the nucleotide physical-chemical properties.The final predictor is constructed with the classifier ensemble,the K base classifiers are obtained by combining the support vector machine(SVM)with reduction subsets,and the reduction algorithm is used to remove the redundant physical-chemical properties and re-encode the RNA sample sequence. Finally,the jackknife test on the benchmark dataset verifies the pseudo dinucleotide composition analysis and auto variance and cross covariance transformation respectively,which are two typical representative algorithms based on nucleotide physical-chemical properties. The experimental results show that the proposed algorithm can significantly improve the overall prediction performance of m6A sites.

参考文献/References:

[1] Yue Y,Liu J,He C. RNA N6-methyladenosine methylation in post-transcriptional gene expression regulation[J]. Genes & Development,2015,29:1343-55.
[2]Dominissini D,Moshitch-Moshkovitz S,Salmon-Divon M,et al. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing[J]. Nature Protocols,2013,8:176-89.
[3]Chen W,Feng P,Ding H,et al. iRNA-methyl:identifying N6-methyladenosine sites using pseudo nucleotide composition[J]. Analytical Biochemistry,2015,490:26-33.
[4]Liu Z,Xiao X,Yu D J,et al. pRNAm-PC:Predicting N6-methyladenosine sites in RNA sequences via physical-chemical properties[J]. Analytical Biochemistry,2015,497:60-67.
[5]Schwartz S,Agarwala S D,Mumbach M R,et al. High-resolution mapping reveals a conserved,widespread,dynamic mRNA methylation program in yeast meiosis[J]. Cell,2013,155:1409-1421.
[6]Jia G,Fu Y,Zhao X,et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO[J]. Nature Chemical Biology,2011(7):885-891.
[7]Yang H,Zheng Y,Li T W,et al. Methionine adenosyltransferase 2B,HuR,and sirtuin 1 protein cross-talk impacts on the effect of resveratrol on apoptosis and growth in liver cancer cells[J]. Journal of Biological Chemistry,2013,288:23161-23170.
[8]Blanco S,Frye M. Role of RNA methyltransferases in tissue renewal and pathology. Current opinion in cell biology[J]. Current Opinion in Cell Biology,2014,31:1-7.
[9]Zhang M,Sun J W,Liu Z,et al. Improving N6-methyladenosine site prediction with heuristic selection of nucleotide physical-chemical properties[J]. Analytical Biochemistry,2016(508):104-113.
[10]Benítez-Caballero M J,Medina J,Ramírez-Poussa E. Attribute reduction in rough set theory and formal concept analysis. Rough sets[C]//IJCRS 2017. Olsztyn,Poland:Springer,2017:513-525.
[11]王宇,杨志荣,杨习贝.决策粗糙集属性约简:一种局部视角方法[J]. 南京理工大学学报,2003,27(5):630-635.
Wang Yu,Yang Zhirong,Yang Xibei. Local attribute reduction approach based on decision-theoretic rough set[J]. Journal of Nanjing University of Science and Technology,2003,27(5):630-635.
[12]徐菲菲,雷景生,毕忠勤,等. 大数据环境下多决策表的区间值全局近似约简[J]. 软件学报,2014,25(9):2119-2135.
Xu Feifei,Lei Jingsheng,Bi Zhongqin,et al. Approaches to approximate reduction with interval-valued multi-decision tables in big data[J]. Journal of Software,2014,25(9):2119-2135.
[13]Jing Y,Li T,Fujita H,et al. An incremental attribute reduction approach based on knowledge granularity with a multi-granulation view[J]. Information Sciences,2017,411:23-28.
[14]郜法启,於东军,沈红斌.基于分类器集成的跨膜蛋白两亲螺旋区域位置预测[J]. 南京理工大学学报,2016,40(4):431-437.
Gao Faqi,Yu Dongjun,Shen Hongbin. Prediction of amphipathic helices in transmembrane proteins by using ensembled classifier[J]. Journal of Nanjing University of Science and Technology,2016,40(4):431-437.
[15]Liu B,Liu F,Fang L,et al. RepRNA:a web server for generating various feature vectors of RNA sequences[J]. Molecular Genetics and Genomics,2016,291(1):473-481.
[16]Chang C C,Lin C J. LIBSVM:A library for support vector machines[J]. Acm Transactions on Intelligent Systems & Technology,2011,2(3):389-396.
[17]Wang G Y,Yu H. Monotonic uncertainty measures for attribute reduction in probabilistic rough set model[J]. International Journal of Approximate Reasoning,2015,59:41-67.
[18]Li H,Li D,Zhai Y,et al. A novel attribute reduction approach for multi-label data based on rough set theory[J]. Information Sciences,2016,367:827-847.
[19]Chen J,Lin Y,Lin G,et al. The relationship between attribute reducts in rough sets and minimal vertex covers of graphs[J]. Information Sciences,2015,325:87-97.
[20]Zhao Y,Yao Y Y. Attribute reduction in decision-theoretic rough set models[J]. Information Sciences,2008,178(17):3356-3373.

备注/Memo

备注/Memo:
收稿日期:2017-12-27 修回日期:2018-01-23
基金项目:国家自然科学基金(61772273; 61373062)
作者简介:张明(1978-),男,博士,副教授,主要研究方向:生物信息学、模式识别等,E-mail:zhangming@just.edu.cn; 通讯作者:於东军(1975-),男,教授,博士生导师,主要研究方向:模式识别、生物信息学等,E-mail:njyudj@njust.edu.cn。
引文格式:张明,徐妍,陈韬,等. 基于核酸物化属性显著性约简的m6A位点识别[J]. 南京理工大学学报,2019,43(2):199-208.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2019-04-26