核小体定位预测的集成学习方法  

Ensemble Learning Method for Nucleosome Localization Prediction

作　　者：陈伟 李杭 李维华[1] CHEN Wei;LI Hang;LI Wei-hua(School of Information Science and Engineering,Yunnan University,Kunming 650500,China)

机构地区：[1]云南大学信息学院,昆明650500

出　　处：《计算机科学》2022年第2期285-291,共7页Computer Science

基　　金：云南省教育厅科学研究基金(2019J0006);云南省创新团队项目(2018HC019)。

摘　　要：核小体定位指DNA双螺旋相对于组蛋白的位置,并在DNA的转录阶段起着重要的调节作用。依靠生物实验的手段测得核小体定位会消耗大量的时间和资源,因此基于计算方法利用DNA序列进行核小体定位预测成为了一个重要的研究方向。针对核小体定位预测中单一模型和单一编码在DNA序列特征表示和学习方面的不足,文中提出了一种端到端的集成深度学习模型FuseENup,利用3种编码方式从多个维度表示DNA数据,利用不同的模型从不同维度提取数据中隐含的关键特征,构造了一种全新的DNA序列表征模型。在4种数据集上进行20倍交叉验证,相比当前针对核小体定位预测问题综合性能最优的模型CORENup,FuseENup的准确度(Accuracy)和精度(Precision)在HS数据集上提高了3%和9%,在DM数据集上提高了2%和6%,在E数据集上提高了1%和4%,相比其他的机器学习和深度学习基准模型,FuseENup具有更好的性能。实验结果表明,FuseENup能提高核小体定位的预测准确度,说明了该方法的有效性和科学性。Nucleosome localization refers to the position of DNA double helix relative to histone,and plays an important regulatory role in DNA transcription.It takes a lot of time and resources to detect nucleosome localization by biological experiments.Therefore,it is an important research direction to predict nucleosome localization by using DNA sequences based on computationalmethods.Aiming at the shortcomings of single model and single code in DNA sequence feature representation and learning in nucleosome location prediction,this paper proposes an end-to-end ensemble deep learning model FuseENup,which uses three coding methods to represent DNA data from multiple dimensions.Different models extract the key features hidden in the data from different dimensions,and construct a new DNA sequence representation model.Performing 20-fold cross-validation on the four data sets,compared to the current model CORENup with the best comprehensive performance for the nucleosome localization prediction problem,the accuracy and precision of FuseENup are improved by 3%and 9%on the HS data set,increases 2%and 6%on the DM data set,1%and 4%on the E data set.Compared with other machine learning and deep learning benchmark models,FuseENup has better performance.Experiments show that FuseENup can improve the prediction accuracy of nucleosomes localization,which shows the effectiveness and scientificity of the method.

关 键 词：核小体定位 深度学习 集成学习方法 DNA序列编码 交叉验证 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] Q811.4[自动化与计算机技术—控制科学与工程]