协同深度网络:基于DNA甲基化的年龄预测的协同卷积神经网络

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Najmeh Sadat Jaddi, Mohammad Saniee Abadeh, Niousha Bagheri Khoulenjani, Salwani Abdullah, MohammadMahdi Ariannejad, Mohd Zakree Ahmad Nazri, Fatemeh Alvankarian
{"title":"协同深度网络:基于DNA甲基化的年龄预测的协同卷积神经网络","authors":"Najmeh Sadat Jaddi,&nbsp;Mohammad Saniee Abadeh,&nbsp;Niousha Bagheri Khoulenjani,&nbsp;Salwani Abdullah,&nbsp;MohammadMahdi Ariannejad,&nbsp;Mohd Zakree Ahmad Nazri,&nbsp;Fatemeh Alvankarian","doi":"10.1049/cit2.70026","DOIUrl":null,"url":null,"abstract":"<p>Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age. In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed. In this research study a convolutional neural network (CNN)-based model optimised by the genetic algorithm (GA) is addressed. This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge between them. This specifically re-starts the training process from a possibly higher-quality point in different iterations and, consequently, causes potentially yeilds better results at each iteration. The method proposed, which is called cooperative deep neural network (Co-DeepNet), is tested on two types of age prediction problems. Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency. As a result, the mean absolute deviation (MAD) is 1.49 and 3.61 years for training and testing data, respectively, when the healthy data is tested. The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data, respectively. The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements (<i>R</i><sup>2</sup>, MAD, MSE and RMSE). The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1118-1134"},"PeriodicalIF":7.3000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70026","citationCount":"0","resultStr":"{\"title\":\"Co-DeepNet: A Cooperative Convolutional Neural Network for DNA Methylation-Based Age Prediction\",\"authors\":\"Najmeh Sadat Jaddi,&nbsp;Mohammad Saniee Abadeh,&nbsp;Niousha Bagheri Khoulenjani,&nbsp;Salwani Abdullah,&nbsp;MohammadMahdi Ariannejad,&nbsp;Mohd Zakree Ahmad Nazri,&nbsp;Fatemeh Alvankarian\",\"doi\":\"10.1049/cit2.70026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age. In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed. In this research study a convolutional neural network (CNN)-based model optimised by the genetic algorithm (GA) is addressed. This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge between them. This specifically re-starts the training process from a possibly higher-quality point in different iterations and, consequently, causes potentially yeilds better results at each iteration. The method proposed, which is called cooperative deep neural network (Co-DeepNet), is tested on two types of age prediction problems. Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency. As a result, the mean absolute deviation (MAD) is 1.49 and 3.61 years for training and testing data, respectively, when the healthy data is tested. The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data, respectively. The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements (<i>R</i><sup>2</sup>, MAD, MSE and RMSE). The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.</p>\",\"PeriodicalId\":46211,\"journal\":{\"name\":\"CAAI Transactions on Intelligence Technology\",\"volume\":\"10 4\",\"pages\":\"1118-1134\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70026\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CAAI Transactions on Intelligence Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70026\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70026","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

利用DNA甲基化随年龄变化的模式来预测每个人的年龄是可能的。本文提出了一种利用DNA甲基化数据求解多元回归问题的年龄预测方法。本文研究了一种基于卷积神经网络(CNN)的遗传算法优化模型。本文利用两个cnn的联合和它们之间的知识交换,将年龄预测作为一个回归问题来增强。这特别地在不同的迭代中从可能更高质量的点重新开始训练过程,因此,在每次迭代中都可能产生更好的结果。该方法被称为合作深度神经网络(Co-DeepNet),并在两类年龄预测问题上进行了测试。使用包含1899个健康血液样本的16个数据集和包含2395个患病血液样本的9个数据集来检验该方法的有效性。因此,对健康数据进行测试时,训练数据和测试数据的平均绝对偏差(MAD)分别为1.49和3.61年。病变血液数据的训练和检测数据的MAD结果分别为3.81年和5.43年。将Co-DeepNet的结果与先前研究中提出的其他六种方法以及使用四种预测精度测量(R2, MAD, MSE和RMSE)的单个CNN进行了比较。通过统计分析,证明了协同深度网络的有效性和结果的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Co-DeepNet: A Cooperative Convolutional Neural Network for DNA Methylation-Based Age Prediction

Co-DeepNet: A Cooperative Convolutional Neural Network for DNA Methylation-Based Age Prediction

Co-DeepNet: A Cooperative Convolutional Neural Network for DNA Methylation-Based Age Prediction

Co-DeepNet: A Cooperative Convolutional Neural Network for DNA Methylation-Based Age Prediction

Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age. In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed. In this research study a convolutional neural network (CNN)-based model optimised by the genetic algorithm (GA) is addressed. This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge between them. This specifically re-starts the training process from a possibly higher-quality point in different iterations and, consequently, causes potentially yeilds better results at each iteration. The method proposed, which is called cooperative deep neural network (Co-DeepNet), is tested on two types of age prediction problems. Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency. As a result, the mean absolute deviation (MAD) is 1.49 and 3.61 years for training and testing data, respectively, when the healthy data is tested. The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data, respectively. The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements (R2, MAD, MSE and RMSE). The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信