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改进的低秩矩阵恢复方法用于预测 miRNA-疾病关联。

Improved low-rank matrix recovery method for predicting miRNA-disease association.

机构信息

College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China.

College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, 411201, China.

出版信息

Sci Rep. 2017 Jul 20;7(1):6007. doi: 10.1038/s41598-017-06201-3.

DOI:10.1038/s41598-017-06201-3
PMID:28729528
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5519594/
Abstract

MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research. Considering limitation of existing computational approaches, we develop improved low-rank matrix recovery (ILRMR) for miRNA-disease association prediction. ILRMR is a global method that can simultaneously prioritize potential association for all diseases and does not require negative samples. ILRMR can also identify promising miRNAs for investigating diseases without any known related miRNA. By integrating miRNA-miRNA similarity information, disease-disease similarity information, and miRNA family information to matrix recovery, ILRMR performs better than other methods in cross validation and case studies.

摘要

微小 RNA（miRNAs）在各种人类疾病中发挥着关键作用，但 miRNA 相关的发病机制仍不完全清楚。揭示 miRNA 与疾病之间的潜在关系是生物医学研究中的一个关键问题。考虑到现有计算方法的局限性，我们开发了改进的低秩矩阵恢复（ILRMR）方法，用于 miRNA-疾病关联预测。ILRMR 是一种全局方法，可同时优先考虑所有疾病的潜在关联，且不需要负样本。ILRMR 还可以识别具有研究潜力的 miRNA，而无需任何已知相关 miRNA。通过整合 miRNA-miRNA 相似性信息、疾病-疾病相似性信息和 miRNA 家族信息到矩阵恢复中，ILRMR 在交叉验证和案例研究中的表现优于其他方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/82561d35cd71/41598_2017_6201_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/e8f8b901f41f/41598_2017_6201_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/149456832b65/41598_2017_6201_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/f84ace676e19/41598_2017_6201_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/82561d35cd71/41598_2017_6201_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/e8f8b901f41f/41598_2017_6201_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/149456832b65/41598_2017_6201_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/f84ace676e19/41598_2017_6201_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f500/5519594/82561d35cd71/41598_2017_6201_Fig4_HTML.jpg

相似文献

[1]
Improved low-rank matrix recovery method for predicting miRNA-disease association.

Sci Rep. 2017-7-20

[2]
An improved random forest-based computational model for predicting novel miRNA-disease associations.

BMC Bioinformatics. 2019-12-3

[3]
NTSMDA: prediction of miRNA-disease associations by integrating network topological similarity.

Mol Biosyst. 2016-6-21

[4]
Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities.

IEEE/ACM Trans Comput Biol Bioinform. 2016-7-7

[5]
RBMMMDA: predicting multiple types of disease-microRNA associations.

Sci Rep. 2015-9-8

[6]
WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

Sci Rep. 2016-2-16

[7]
An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy.

RNA Biol. 2019-1-28

[8]
A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction.

Mol Biosyst. 2017-5-30

[9]
MicroRNAs and complex diseases: from experimental results to computational models.

Brief Bioinform. 2019-3-22

[10]
MDAlmc: A Novel Low-rank Matrix Completion Model for MiRNADisease Association Prediction by Integrating Similarities among MiRNAs and Diseases.

Curr Gene Ther. 2023

引用本文的文献

[1]
Machine learning in the development of targeting microRNAs in human disease.

Front Genet. 2023-1-4

[2]
HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction.

Int J Mol Sci. 2022-10-29

[3]
A novel information diffusion method based on network consistency for identifying disease related microRNAs.

RSC Adv. 2018-10-30

[4]
LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.

RSC Adv. 2019-9-20

[5]
A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.

PLoS One. 2021

[6]
MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.

Front Genet. 2020-4-30

[7]
Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.

Front Genet. 2019-4-26

[8]
MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association.

Front Genet. 2018-12-10

[9]
FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association.

BMC Genomics. 2018-12-31

[10]
Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.

Sci Rep. 2018-4-24

本文引用的文献

[1]
A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction.

Mol Biosyst. 2017-5-30

[2]
RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.

RNA Biol. 2017-7-3

[3]
PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction.

PLoS Comput Biol. 2017-3-24

[4]
Interplay between miRNAs and human diseases.

J Cell Physiol. 2018-3

[5]
MCMDA: Matrix completion for MiRNA-disease association prediction.

Oncotarget. 2017-3-28

[6]
miRNA nanotherapeutics for cancer.

Drug Discov Today. 2017-2

[7]
Network Consistency Projection for Human miRNA-Disease Associations Inference.

Sci Rep. 2016-10-25

[8]
HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction.

Oncotarget. 2016-10-4

[9]
Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources.

IEEE/ACM Trans Comput Biol Bioinform. 2017

[10]
WBSMDA: Within and Between Score for MiRNA-Disease Association prediction.

Sci Rep. 2016-2-16

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