Predicting Long Non-coding RNA-disease Associations using Multiple Features and Deep Learning

{"title":"Predicting Long Non-coding RNA-disease Associations using Multiple Features and Deep Learning","authors":"","doi":"10.32913/mic-ict-research.v2022.n2.1069","DOIUrl":null,"url":null,"abstract":"Various long non-coding RNAs have been shownto play crucial roles in different biological processes includingcell cycle control, transcription, translation, epigenetic regulation, splicing, differentiation, immune response and so forthin the human body. Discovering lncRNA-disease associationspromotes the awareness of human complex disease at molecular level and support the diagnosis, treatment and prevention of complex diseases. It is costly, laboratory and timeconsuming to discover and verify lncRNA-disease associationsby biological experiments. Therefore, it is crucial to develop acomputational method to predict lncRNA-disease associationsto save time and resources. In this paper, we proposed a newmethod to predict lncRNA-disease associations using multiplefeatures and deep learning. Our method uses a weighted????-nearest known neighbors algorithm as a pre-processingstep to eliminate the impact of sparsity data problem. Andit combines the linear and non-linear features extracted bysingular value decomposition and deep learning techniques,respectively, to obtain better prediction performance. Ourproposed method achieves a decisive performance with thebest AUC and AUPR values of 0.9702 and 0.8814, respectively,under LOOCV experiments. It is superior to other stateof-the-art SDLDA and NCPLDA methods in both AUC andAUPR evaluation metrics. It could be considered as a powerfultool to predict lncRNA-disease associations.","PeriodicalId":432355,"journal":{"name":"Research and Development on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research and Development on Information and Communication Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32913/mic-ict-research.v2022.n2.1069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Various long non-coding RNAs have been shownto play crucial roles in different biological processes includingcell cycle control, transcription, translation, epigenetic regulation, splicing, differentiation, immune response and so forthin the human body. Discovering lncRNA-disease associationspromotes the awareness of human complex disease at molecular level and support the diagnosis, treatment and prevention of complex diseases. It is costly, laboratory and timeconsuming to discover and verify lncRNA-disease associationsby biological experiments. Therefore, it is crucial to develop acomputational method to predict lncRNA-disease associationsto save time and resources. In this paper, we proposed a newmethod to predict lncRNA-disease associations using multiplefeatures and deep learning. Our method uses a weighted????-nearest known neighbors algorithm as a pre-processingstep to eliminate the impact of sparsity data problem. Andit combines the linear and non-linear features extracted bysingular value decomposition and deep learning techniques,respectively, to obtain better prediction performance. Ourproposed method achieves a decisive performance with thebest AUC and AUPR values of 0.9702 and 0.8814, respectively,under LOOCV experiments. It is superior to other stateof-the-art SDLDA and NCPLDA methods in both AUC andAUPR evaluation metrics. It could be considered as a powerfultool to predict lncRNA-disease associations.
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
利用多特征和深度学习预测长链非编码rna与疾病的关联
多种长链非编码rna在人体细胞周期控制、转录、翻译、表观遗传调控、剪接、分化、免疫应答等生物学过程中发挥着重要作用。lncrna与疾病关联的发现促进了人类在分子水平上对复杂疾病的认识,为复杂疾病的诊断、治疗和预防提供了支持。通过生物学实验来发现和验证lncrna与疾病的关联是昂贵的、实验室的和耗时的。因此,开发一种预测lncrna -疾病关联的计算方法以节省时间和资源至关重要。在本文中,我们提出了一种利用多特征和深度学习来预测lncrna -疾病关联的新方法。我们的方法使用一个加权的????-采用最近邻算法作为预处理步骤,消除数据稀疏性问题的影响。并将奇异值分解提取的线性和非线性特征与深度学习技术相结合,以获得更好的预测性能。在LOOCV实验下,该方法的最佳AUC和AUPR值分别为0.9702和0.8814,具有决定性的性能。该方法在AUC和aupr评价指标上都优于其他最先进的SDLDA和NCPLDA方法。它可以被认为是预测lncrna与疾病关联的有力工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Một thuật toán định tuyến cân bằng năng lượng trong mạng cảm biến không dây dựa trên SDN Location Fusion and Data Augmentation for Thoracic Abnormalites Detection in Chest X-Ray Images A review of cyber security risk assessment for web systems during its deployment and operation Surveying Some Metaheuristic Algorithms For Solving Maximum Clique Graph Problem Deep Learning of Image Representations with Convolutional Neural Networks Autoencoder for Image Retrieval with Relevance Feedback
×
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