Literature DB >> 29045685

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

Xing Chen1, Di Xie2, Qi Zhao2, Zhu-Hong You3.   

Abstract

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.
© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  biological network; complex disease; computational model; machine learning; microRNA; microRNA–disease association prediction

Mesh:

Substances:

Year:  2019        PMID: 29045685     DOI: 10.1093/bib/bbx130

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  145 in total

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

Authors:  Chun-Chun Wang; Xing Chen; Jun Yin; Jia Qu
Journal:  RNA Biol       Date:  2019-01-28       Impact factor: 4.652

2.  Predicting microRNA-disease associations using bipartite local models and hubness-aware regression.

Authors:  Xing Chen; Jun-Yan Cheng; Jun Yin
Journal:  RNA Biol       Date:  2018-09-19       Impact factor: 4.652

3.  Inferring Potential CircRNA-Disease Associations via Deep Autoencoder-Based Classification.

Authors:  K Deepthi; A S Jereesh
Journal:  Mol Diagn Ther       Date:  2020-11-06       Impact factor: 4.074

Review 4.  Recent advances on the machine learning methods in predicting ncRNA-protein interactions.

Authors:  Lin Zhong; Meiqin Zhen; Jianqiang Sun; Qi Zhao
Journal:  Mol Genet Genomics       Date:  2020-10-02       Impact factor: 3.291

5.  SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association.

Authors:  Yan Zhao; Xing Chen; Jun Yin; Jia Qu
Journal:  RNA Biol       Date:  2019-11-27       Impact factor: 4.652

6.  MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data.

Authors:  Jie Ping; Olufunmilola Oyebamiji; Hui Yu; Scott Ness; Jeremy Chien; Fei Ye; Huining Kang; David Samuels; Sergey Ivanov; Danqian Chen; Ying-Yong Zhao; Yan Guo
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

7.  IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction.

Authors:  Qi Zhao; Yue Zhang; Huan Hu; Guofei Ren; Wen Zhang; Hongsheng Liu
Journal:  Front Genet       Date:  2018-07-04       Impact factor: 4.599

8.  ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs.

Authors:  Wenliang Zhang; Guocai Yao; Jianbo Wang; Minglei Yang; Jing Wang; Haiyue Zhang; Weizhong Li
Journal:  RNA Biol       Date:  2020-03-26       Impact factor: 4.652

9.  A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs.

Authors:  Lin Yuan; Jing Zhao; Tao Sun; Zhen Shen
Journal:  BMC Bioinformatics       Date:  2021-06-16       Impact factor: 3.169

10.  MiR-148a-3p may contribute to flawed decidualization in recurrent implantation failure by modulating HOXC8.

Authors:  Qian Zhang; Tianxiang Ni; Yujie Dang; Lingling Ding; Jingjing Jiang; Jing Li; Mingdi Xia; Na Yu; Jinlong Ma; Junhao Yan; Zi-Jiang Chen
Journal:  J Assist Reprod Genet       Date:  2020-08-08       Impact factor: 3.412

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