[1]崔穎,施丹丹,徐澤龍,等.卷積神經網絡在核小體定位識別中的應用[J].哈爾濱工程大學學報,2021,42(5):751-758.[doi:10.11990/jheu.202001043]
 CUI Ying,SHI Dandan,XU Zelong,et al.Application of convolutional neural network based on Z-Curve theory in identifying nucleosome positioning[J].Journal of Harbin Engineering University,2021,42(5):751-758.[doi:10.11990/jheu.202001043]
點擊復制

卷積神經網絡在核小體定位識別中的應用(/HTML)
分享到:

《哈爾濱工程大學學報》[ISSN:1006-6977/CN:61-1281/TN]

卷:
42
期數:
2021年5期
頁碼:
751-758
欄目:
出版日期:
2021-05-05

文章信息/Info

Title:
Application of convolutional neural network based on Z-Curve theory in identifying nucleosome positioning
作者:
崔穎12 施丹丹1 徐澤龍1 張兆功2 李建中2
1. 哈爾濱醫科大學 生物信息科學與技術學院, 黑龍江 哈爾濱 150086;
2. 黑龍江大學 計算機科學與技術學院, 黑龍江 哈爾濱 150080
Author(s):
CUI Ying12 SHI Dandan1 XU Zelong1 ZHANG Zhaogong2 LI Jianzhong2
1. Harbin Medical University, College of Bioinformatics Science and Technology, Harbin 150086, China;
2. Heilongjiang University, College of Computer Science and Technology, Harbin 150080, China
關鍵詞:
計算生物學卷積神經網絡Z曲線理論核小體DNA序列連接區
分類號:
TP391
DOI:
10.11990/jheu.202001043
文獻標志碼:
A
摘要:
為更準確識別核小體定位,本文提出一種基于Z曲線理論(Z-Curve)的卷積神經網絡(CNN)方法,稱為ZCN方法。ZCN方法以Z曲線三維坐標矩陣表示核小體序列特征,通過十倍交叉驗證,進行卷積神經網絡方法進行模型訓練和驗證,使用標準評估指標進行性能評價。結果表明:ZCN方法在酵母中具有良好的識別效能,敏感性Sn、準確性Sp、ROC曲線面積分別為92.4%、90.2%和0.970 4,可推廣到人類、線蟲和果蠅的核小體定位識別中,其ROC曲線面積分別為0.796、0.940和0.772,與其他方法比較,進一步證實ZCN方法具有較好的識別效能和可推廣性。在酵母全基因組進行核小體定位預測,發現16條染色體的預測準確率均值為78.83%,在基因GAL和GAL10中進行核小體定位預測,研究了降低假陽性的方法,給出了預測核小體定位的圖譜。ZCN方法為研究核小體定位識別、預測及功能分析提供了有價值的方法和指導。

參考文獻/References:

[1] SARIGVL M, OZYILDIRIM B M, AVCI M. Differential convolutional neural network[J]. Neural networks, 2019, 116:279-287.
[2] 張功國, 吳建, 易億,等. 基于集成卷積神經網絡的交通標志識別[J].重慶郵電大學學報(自然科學版), 2019, 31(04):571-577.ZHANG Gongguo,WU Jian,YI Yi, et al. Traffic sign recognition based on ensemble convolutional neural network[J]. Journal of Chongqing University of Posts and Telecommunications(natural science edition), 2019, 31(4):571-577.
[3] TABERLAY P C, STATHAM A L, KELLY T K, et al. Reconfiguration of nucleosome-depleted regions at distal regulatory elements accompanies DNA methylation of enhancers and insulators in cancer[J]. Genome research, 2014, 24(9):1421-1432.
[4] FARMAN F U, IQBAL M, AZAM M, et al. Nucleosomes positioning around transcriptional start site of tumor suppressor (Rbl2/p130) gene in breast cancer[J]. Molecular biology reports, 2018, 45(2):185-194.
[5] BUCKWALTER J M, NOROUZI D, HARUTYUNYAN A, et al. Regulation of chromatin folding by conformational variations of nucleosome linker DNA[J]. Nucleic acids research, 2017, 45(16):9372-9387.
[6] MURUGAN R. Theory of site-specific DNA-protein interactions in the presence of nucleosome roadblocks[J]. Biophysical journal, 2018, 114(11):2516-2529.
[7] NOCETTI N, WHITEHOUSE I. Nucleosome repositioning underlies dynamic gene expression[J]. Genes & development, 2016, 30(6):660-672.
[8] BAI Lu, MOROZOV A V. Gene regulation by nucleosome positioning[J]. Trends in genetics, 2010, 26(11):476-483.
[9] EATON M L, GALANI K, KANG S, et al. Conserved nucleosome positioning defines replication origins[J]. Genes & development, 2010, 24(8):748-753.
[10] YING Hua, EPPS J, WILLIAMS R, et al. Evidence that localized variation in primate sequence divergence arises from an influence of nucleosome placement on DNA Repair[J]. Molecular biology and evolution, 2010, 27(3):637-649.
[11] BEVINGTON S, BOYES J. Transcription-coupled eviction of histones H2A/H2B governs V(D)J recombination[J]. The EMBO journal, 2013, 32(10):1381-1392.
[12] XING Yongqiang, LIU Guoqing, ZHAO Xiujuan, et al. An analysis and prediction of nucleosome positioning based on information content[J]. Chromosome research, 2013, 21(1):63-74.
[13] LIELEG C, KRIETENSTEIN N, WALKER M, et al. Nucleosome positioning in yeasts:methods, maps, and mechanisms[J]. Bioinformatics, 2015, 124(2):131-151.
[14] ZHANG Juhua, PENG Wenbo, WANG Lei. LeNup:learning nucleosome positioning from DNA sequences with improved convolutional neural networks[J]. Bioinformatics, 2018, 34(10):1705-1712.
[15] 張任, 張春霆. Z曲線, 顯示和分析DNA序列的直觀工具[J]. 自然雜志, 1995, 17(1):34-37. ZHANG Ren, ZHANG Chunting. Z-curve:an intuitive tool for visualizing and analyzing the DNA sequences[J]. Chinese journal of nature, 1995, 17(1):34-37.
[16] 劉超, 張曉暉, 胡清平. 圖像超分辨率卷積神經網絡加速算法[J]. 國防科技大學學報, 2019, 41(2):91-97. LIU Chao, ZHANG Xiaohui, HU Qingping. Image super resolution convolution neural network acceleration algorithm[J]. Journal of National University of Defense Technology, 2019, 41(2):91-97.
[17] 楊軍,王亦民.基于深度卷積神經網絡的三維模型識別[J].重慶郵電大學學報(自然科學版), 2019, 31(2):253-260.YANG Jun,WANG Yimin. 3D model recognition based on depth convolution neural network[J]. Journal of Chongqing University of Posts and Telecommunications(natural science edition), 2019, 31(2):253-260.
[18] GUO Shouhui, DENG Enze, XU Liqin, et al. iNuc-PseKNC:a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition[J]. Bioinformatics, 2014, 30(11):1522-1529.
[19] CHEN Wei, LIN Hao, FENG Pengmian, et al. iNuc-PhysChem:a sequence-based predictor for identifying nucleosomes via physicochemical properties[J]. PLoS one, 2012, 7(10):e47843.
[20] CHEN Wei, FENG Pengmian, DING Hui, et al. Using deformation energy to analyze nucleosome positioning in genomes[J]. Genomics, 2016, 107(2/3):69-75.
[21] TAHIR M, HAYAT M. iNuc-STNC:a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou’s PseAAC[J]. Molecular biosystems, 2016, 12(8):2587-2593.
[22] KAROLCHIK D, BAERTSCH R, DIEKHANS M, et al. The UCSC genome browser database[J]. Nucleic acids research, 2003, 31(1):51-54.
[23] ZHOU Xu, BLOCKER A W, AIROLDI E M, et al. A computational approach to map nucleosome positions and alternative chromatin states with base pair resolution[J]. eLife, 2016, 5:e16970.

相似文獻/References:

[1]畢曉君,馮雪赟.基于改進深度學習模型C-GRBM的人體行為識別[J].哈爾濱工程大學學報,2018,39(01):156.[doi:10.11990/jheu.201612051]
 BI Xiaojun,FENG Xueyun.Human action recognition based on improved depth learning model C-GRBM[J].Journal of Harbin Engineering University,2018,39(5):156.[doi:10.11990/jheu.201612051]
[2]黃斌,陳仁文,周秦邦,等.SR-CNN融合決策的眼部狀態識別方法[J].哈爾濱工程大學學報,2018,39(07):1233.[doi:10.11990/jheu.201612071]
 HUANG Bin,CHEN Renwen,ZHOU Qinbang,et al.Research on eye state recognition using SR-CNN fusion-decision method[J].Journal of Harbin Engineering University,2018,39(5):1233.[doi:10.11990/jheu.201612071]
[3]王言鵬,楊飏,姚遠.用于內河船舶目標檢測的單次多框檢測器算法[J].哈爾濱工程大學學報,2019,40(07):1258.[doi:10.11990/jheu.201805057]
 WANG Yanpeng,YANG Yang,YAO Yuan.Single shot multibox detector for ships detection in inland waterway[J].Journal of Harbin Engineering University,2019,40(5):1258.[doi:10.11990/jheu.201805057]
[4]王念濱,何鳴,王紅濱,等.適用于水下目標識別的快速降維卷積模型[J].哈爾濱工程大學學報,2019,40(07):1327.[doi:10.11990/jheu.201805113]
 WANG Nianbin,HE Ming,WANG Hongbin,et al.A fast reduced-dimension convolution model for underwater target recognition[J].Journal of Harbin Engineering University,2019,40(5):1327.[doi:10.11990/jheu.201805113]
[5]畢曉君,喬偉征.基于改進深度學習模型C-NTM的腦電魯棒特征學習[J].哈爾濱工程大學學報,2019,40(09):1642.[doi:10.11990/jheu.201808069]
 BI Xiaojun,QIAO Weizheng.Learning robust features from EEG based on improved deep-learning model C-NTM[J].Journal of Harbin Engineering University,2019,40(5):1642.[doi:10.11990/jheu.201808069]
[6]王立鵬,張智,蘇麗,等.融合優選圖案的深度學習目標識別及定位技術[J].哈爾濱工程大學學報,2020,41(4):549.[doi:10.11990/jheu.201901047]
 WANG Lipeng,ZHANG Zhi,SU Li,et al.Target recognition and location using deep learning based on selected patterns[J].Journal of Harbin Engineering University,2020,41(5):549.[doi:10.11990/jheu.201901047]
[7]曹懷剛,任群言,郭圣明,等.卷積神經網絡單矢量水聽器方位估計[J].哈爾濱工程大學學報,2020,41(10):1524.[doi:10.11990/jheu.202007043]
 CAO Huaigang,REN Qunyan,GUO Shengming,et al.Source azimuth estimation with single vector sensor based on convolutional neural network[J].Journal of Harbin Engineering University,2020,41(5):1524.[doi:10.11990/jheu.202007043]
[8]汪夢婷,袁飛,程恩.魚類目標的密度估計模型[J].哈爾濱工程大學學報,2020,41(10):1545.[doi:10.11990/jheu.202007028]
 WANG Mengting,YUAN Fei,CHENG En.Density estimation model for fish objects[J].Journal of Harbin Engineering University,2020,41(5):1545.[doi:10.11990/jheu.202007028]
[9]孫光民,陳佳陽,李冰,等.雙尺度網絡高分辨率樓面影像微小缺陷檢測[J].哈爾濱工程大學學報,2021,42(2):286.[doi:10.11990/jheu.201909096]
 SUN Guangmin,CHEN Jiayang,LI Bing,et al.Detection of small defects on a building wall surface from high-resolution images using dual-scale neural networks[J].Journal of Harbin Engineering University,2021,42(5):286.[doi:10.11990/jheu.201909096]
[10]于凌濤,夏永強,閆昱晟,等.利用卷積神經網絡分類乳腺癌病理圖像[J].哈爾濱工程大學學報,2021,42(4):567.[doi:10.11990/jheu.201909052]
 YU Lingtao,XIA Yongqiang,YAN Yusheng,et al.Breast cancer pathological image classification based on a convolutional neural network[J].Journal of Harbin Engineering University,2021,42(5):567.[doi:10.11990/jheu.201909052]

備注/Memo

備注/Memo:
收稿日期:2020-01-28。
基金項目:國家自然科學基金重點項目(61832003).
作者簡介:崔穎,女,講師,博士;李建中,教授,博士生導師.
通訊作者:李建中,E-mail:lijzh@hit.edu.cn.
更新日期/Last Update: 2021-04-26
看真人视频a级毛片