• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Frontiers in Genetics >A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square
【2h】

A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square

机译：基于对称非负矩阵分解和Kronecker正则最小二乘的潜在miRNA-疾病关联识别的新计算方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Increasing evidence has indicated that microRNAs (miRNAs) are associated with numerous human diseases. Studying the associations between miRNAs and diseases contributes to the exploration of effective diagnostic and treatment approaches for diseases. Unfortunately, the use of biological experiments to reveal the potential associations between miRNAs and diseases is time consuming and costly. Therefore, it is very necessary to use simple and efficient calculation models to predict potential disease-related miRNAs. Considering the limitations of other previous methods, we proposed a novel computational model of Symmetric Nonnegative Matrix Factorization for MiRNA-Disease Association prediction (SNMFMDA) to reveal the relation of miRNA-disease pairs. SNMFMDA could be applied to predict miRNAs associated with new diseases. Compared to the direct use of the integrated similarity in previous computational models, the integrated similarity need to be interpolated by symmetric non-negative matrix factorization (SymNMF) before application in SNMFMDA, and the relevant probability of disease-miRNA was obtained mainly through Kronecker regularized least square (KronRLS) method in our model. What's more, the AUC of global leave-one-out cross validation (LOOCV) reached 0.9007, and the AUC based on local LOOCV was 0.8426. Besides, the mean and the standard deviation of AUCs achieved 0.8830 and 0.0017 respectively in 5-fold cross validation. All of the above results demonstrated the superior prediction performance of SNMFMDA. We also conducted three different case studies on Esophageal Neoplasms, Breast Neoplasms and Lung Neoplasms, and 49, 49, and 48 of the top 50 of their predicted miRNAs respectively were confirmed by databases or related literatures. It could be expected that SNMFMDA would be a model with the ability to predict disease-related miRNAs efficiently and accurately.
机译：越来越多的证据表明，microRNA（miRNA）与许多人类疾病有关。研究miRNA与疾病之间的关联有助于探索有效的疾病诊断和治疗方法。不幸的是，利用生物学实验揭示miRNA与疾病之间的潜在联系既耗时又昂贵。因此，非常有必要使用简单有效的计算模型来预测潜在的疾病相关miRNA。考虑到其他先前方法的局限性，我们提出了一种新的对称非负矩阵因式分解模型，用于MiRNA疾病关联预测（SNMFMDA），以揭示miRNA疾病对的关系。 SNMFMDA可用于预测与新疾病相关的miRNA。与以前的计算模型中直接使用整合相似度相比，整合相似度需要在应用SNMFMDA之前通过对称非负矩阵分解（SymNMF）进行插值，并且疾病miRNA的相关概率主要通过正则化的Kronecker获得模型中的最小二乘（KronRLS）方法。此外，全球留一法交叉验证（LOOCV）的AUC达到0.9007，而基于本地LOOCV的AUC为0.8426。此外，在5倍交叉验证中，AUC的平均值和标准偏差分别达到0.8830和0.0017。以上所有结果证明了SNMFMDA的优越的预测性能。我们还对食管肿瘤，乳腺肿瘤和肺肿瘤进行了三个不同的案例研究，分别通过数据库或相关文献证实了其预测的miRNA的前50个中的49个，49个和48个。可以预期SNMFMDA将成为能够有效且准确地预测与疾病相关的miRNA的模型。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号