Literature DB >> 26134276

Similarity computation strategies in the microRNA-disease network: a survey.

Quan Zou, Jinjin Li, Li Song, Xiangxiang Zeng, Guohua Wang.   

Abstract

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.
© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  machine learning; microRNA; microRNA–disease relationship; network

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Year:  2015        PMID: 26134276     DOI: 10.1093/bfgp/elv024

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  98 in total

1.  Clustering and classification methods for single-cell RNA-sequencing data.

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

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Journal:  Front Genet       Date:  2018-07-04       Impact factor: 4.599

5.  Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis.

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Review 6.  Role of microRNAs in the pathophysiology of addiction.

Authors:  Austin M Gowen; Katherine E Odegaard; Jordan Hernandez; Subhash Chand; Sneh Koul; Gurudutt Pendyala; Sowmya V Yelamanchili
Journal:  Wiley Interdiscip Rev RNA       Date:  2020-12-17       Impact factor: 9.957

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Journal:  Front Genet       Date:  2021-03-31       Impact factor: 4.599

8.  Comparative Screening of Digestion Tract Toxic Genes in Proteus mirabilis.

Authors:  Xiaolu Shi; Yiman Lin; Yaqun Qiu; Yinghui Li; Min Jiang; Qiongcheng Chen; Yixiang Jiang; Jianhui Yuan; Hong Cao; Qinghua Hu; Shenghe Huang
Journal:  PLoS One       Date:  2016-03-24       Impact factor: 3.240

9.  Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding.

Authors:  Wei Peng; Jielin Du; Wei Dai; Wei Lan
Journal:  Front Cell Dev Biol       Date:  2021-06-10

10.  Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.

Authors:  Li Wang; Cheng Zhong
Journal:  Biomed Res Int       Date:  2021-02-23       Impact factor: 3.411

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