Literature DB >> 25016381

From miRNA regulation to miRNA-TF co-regulation: computational approaches and challenges.

Thuc Duy Le, Lin Liu, Junpeng Zhang, Bing Liu, Jiuyong Li.   

Abstract

microRNAs (miRNAs) are important gene regulators. They control a wide range of biological processes and are involved in several types of cancers. Thus, exploring miRNA functions is important for diagnostics and therapeutics. To date, there are few feasible experimental techniques for discovering miRNA regulatory mechanisms. Alternatively, predictions of miRNA-mRNA regulatory relationships by computational methods have increasingly achieved promising results. Computational approaches are proving their ability as effective tools in reducing the number of biological experiments that must be conducted and to assist with the design of the experiments. In this review, we categorize and review different computational approaches to identify miRNA activities and functions, including the co-regulation of miRNAs and transcription factors. Our main focuses are on the recent approaches that use multiple data types for exploring miRNA functions. We discuss the remaining challenges in the evaluation and selection of models based on the results from a case study. Finally, we analyse the remaining challenges of each computational approach and suggest some future research directions.
© The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  causality discovery; co-regulation; data integration; miRNA; miRNA target; transcription factor

Mesh:

Substances:

Year:  2014        PMID: 25016381     DOI: 10.1093/bib/bbu023

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


  11 in total

1.  Systematic prediction of target genes and pathways in cervical cancer from microRNA expression data.

Authors:  Rui Chen; Yong-Hua Shi; Hong Zhang; Jian-Yun Hu; Yi Luo
Journal:  Oncol Lett       Date:  2018-04-25       Impact factor: 2.967

2.  Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets.

Authors:  Sora Yoon; Hai C T Nguyen; Woobeen Jo; Jinhwan Kim; Sang-Mun Chi; Jiyoung Park; Seon-Young Kim; Dougu Nam
Journal:  Nucleic Acids Res       Date:  2019-05-21       Impact factor: 16.971

3.  Systems biology study of transcriptional and post-transcriptional co-regulatory network sheds light on key regulators involved in important biological processes in Citrus sinensis.

Authors:  Ehsan Khodadadi; Ali Ashraf Mehrabi; Ali Najafi; Saber Rastad; Ali Masoudi-Nejad
Journal:  Physiol Mol Biol Plants       Date:  2017-02-10

4.  Ensemble Methods for MiRNA Target Prediction from Expression Data.

Authors:  Thuc Duy Le; Junpeng Zhang; Lin Liu; Jiuyong Li
Journal:  PLoS One       Date:  2015-06-26       Impact factor: 3.240

5.  miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships.

Authors:  Thuc Duy Le; Junpeng Zhang; Lin Liu; Huawen Liu; Jiuyong Li
Journal:  PLoS One       Date:  2015-12-30       Impact factor: 3.240

6.  An Ensemble Method to Predict Target Genes and Pathways in Uveal Melanoma.

Authors:  Chao Wei; Lei Wang; Han Zhang
Journal:  Open Life Sci       Date:  2018-04-10       Impact factor: 0.938

7.  Protein phosphatase 1 regulatory subunit 3G (PPP1R3G) correlates with poor prognosis and immune infiltration in lung adenocarcinoma.

Authors:  Xingli Zhuo; Lan Chen; Zongwei Lai; Jiansheng Liu; Shengjun Li; Ahu Hu; Yuansheng Lin
Journal:  Bioengineered       Date:  2021-12       Impact factor: 3.269

8.  Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.

Authors:  Junpeng Zhang; Lin Liu; Taosheng Xu; Wu Zhang; Chunwen Zhao; Sijing Li; Jiuyong Li; Nini Rao; Thuc Duy Le
Journal:  BMC Bioinformatics       Date:  2021-12-02       Impact factor: 3.169

9.  Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles.

Authors:  Weijia Zhang; Thuc Duy Le; Lin Liu; Zhi-Hua Zhou; Jiuyong Li
Journal:  PLoS One       Date:  2016-04-11       Impact factor: 3.240

10.  Predicting miRNA targets for hepatocellular carcinoma with an integrated method.

Authors:  Yi-Hua Shi; Tian-Fu Wen; De-Shuang Xiao; Ling-Bo Dai; Jun Song
Journal:  Transl Cancer Res       Date:  2020-03       Impact factor: 1.241

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