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, Mohammad Saniee Abadeh, Niousha Bagheri Khoulenjani, Salwani Abdullah, MohammadMahdi Ariannejad, Mohd Zakree Ahmad Nazri, 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, Mohammad Saniee Abadeh, Niousha Bagheri Khoulenjani, Salwani Abdullah, MohammadMahdi Ariannejad, Mohd Zakree Ahmad Nazri, 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}
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.
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.