基于1D-ResCNN-PLS的中药剂量效应预测优化方法

IF 1.7 4区 医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2024-10-24 DOI:10.1080/10255842.2024.2417203
Wangping Xiong, Jiasong Pan, Zhaoyang Liu, Jianqiang Du, Yimin Zhu, Jigen Luo, Ming Yang, Xian Zhou
{"title":"基于1D-ResCNN-PLS的中药剂量效应预测优化方法","authors":"Wangping Xiong, Jiasong Pan, Zhaoyang Liu, Jianqiang Du, Yimin Zhu, Jigen Luo, Ming Yang, Xian Zhou","doi":"10.1080/10255842.2024.2417203","DOIUrl":null,"url":null,"abstract":"<p><p>We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An optimized method for dose-effect prediction of traditional Chinese medicine based on 1D-ResCNN-PLS.\",\"authors\":\"Wangping Xiong, Jiasong Pan, Zhaoyang Liu, Jianqiang Du, Yimin Zhu, Jigen Luo, Ming Yang, Xian Zhou\",\"doi\":\"10.1080/10255842.2024.2417203\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.</p>\",\"PeriodicalId\":50640,\"journal\":{\"name\":\"Computer Methods in Biomechanics and Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Methods in Biomechanics and Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10255842.2024.2417203\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2024.2417203","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

我们引入了一维(1D)残差卷积神经网络与偏最小二乘法(1D-ResCNN-PLS)来解决中药剂量效应关系数据中的协方差和非线性问题。该模型结合了一维卷积层和残差块来提取非线性特征,并采用偏最小二乘法(PLS)进行预测。该模型在麻杏石甘汤数据集上进行了测试,其性能明显优于传统模型,获得了较高的准确度、灵敏度、特异度和 AUC 值,均方误差也大幅降低。我们的研究结果证实了该模型在非线性数据处理中的有效性,并证明了它在公共数据集中更广泛应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An optimized method for dose-effect prediction of traditional Chinese medicine based on 1D-ResCNN-PLS.

We introduce a one-dimensional (1D) residual convolutional neural network with Partial Least Squares (1D-ResCNN-PLS) to solve the covariance and nonlinearity problems in traditional Chinese medicine dose-effect relationship data. The model combines a 1D convolutional layer with a residual block to extract nonlinear features and employs PLS for prediction. Tested on the Ma Xing Shi Gan Decoction datasets, the model significantly outperformed conventional models, achieving high accuracies, sensitivities, specificities, and AUC values, with considerable reductions in mean square error. Our results confirm its effectiveness in nonlinear data processing and demonstrate potential for broader application across public datasets.

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
CiteScore
4.10
自引率
6.20%
发文量
179
审稿时长
4-8 weeks
期刊介绍: The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
期刊最新文献
Accurate detection of gait events using neural networks and IMU data mimicking real-world smartphone usage. Exploring coronavirus sequence motifs through convolutional neural network for accurate identification of COVID-19. Coexistence of horizontal bone loss and dehiscence with the bundle and conventional fiber post: a finite element analysis. Effects of a soft back exoskeleton on lower lumbar spine loads during manual materials handling: a musculoskeletal modelling study. Mechanical effect of taper position in abutment hole and screw taper angles on implant system and peri-implant tissue: a finite element analysis.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1