Literature DB >> 25189779

Gene regulatory networks by transcription factors and microRNAs in breast cancer.

Sheng Qin1, Fei Ma2, Liming Chen2.   

Abstract

MOTIVATION: Gene regulatory networks (GRNs) affect numerous cellular processes and every process of life, and abnormalities of GRN lead to breast cancer. Transcription factors (TFs) and microRNAs (miRNAs) are two of the best-studied gene regulatory mechanisms. However, the architecture and feature of GRNs by TFs and miRNAs in breast cancer and its subtypes were unknown. In this study, we investigated the GRNs by TFs and miRNAs with emphasis on breast cancer classifier genes at system level.
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Year:  2014        PMID: 25189779     DOI: 10.1093/bioinformatics/btu597

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  18 in total

1.  The oncogenic and prognostic potential of eight microRNAs identified by a synergetic regulatory network approach in lung cancer.

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Journal:  BMC Genomics       Date:  2015-05-26       Impact factor: 3.969

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5.  Gene Regulatory Networks Reconstruction Using the Flooding-Pruning Hill-Climbing Algorithm.

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Journal:  PeerJ       Date:  2018-11-12       Impact factor: 2.984

7.  MicroRNA and transcription factor mediated regulatory network analysis reveals critical regulators and regulatory modules in myocardial infarction.

Authors:  Guangde Zhang; Hongbo Shi; Lin Wang; Meng Zhou; Zhenzhen Wang; Xiaoxia Liu; Liang Cheng; Weimin Li; Xueqi Li
Journal:  PLoS One       Date:  2015-08-10       Impact factor: 3.240

8.  Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells.

Authors:  Hongbo Shi; Guangde Zhang; Jing Wang; Zhenzhen Wang; Xiaoxia Liu; Liang Cheng; Weimin Li
Journal:  PLoS One       Date:  2016-07-01       Impact factor: 3.240

9.  An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection.

Authors:  Linlin Xing; Maozu Guo; Xiaoyan Liu; Chunyu Wang; Lei Wang; Yin Zhang
Journal:  BMC Genomics       Date:  2017-11-17       Impact factor: 3.969

10.  Systematic identification and analysis of dysregulated miRNA and transcription factor feed-forward loops in hypertrophic cardiomyopathy.

Authors:  Hongbo Shi; Jiayao Li; Qiong Song; Liang Cheng; Haoran Sun; Wenjing Fan; Jianfei Li; Zhenzhen Wang; Guangde Zhang
Journal:  J Cell Mol Med       Date:  2018-10-19       Impact factor: 5.310

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