scholarly journals DiffGRN: differential gene regulatory network analysis

2018 ◽  
Vol 20 (4) ◽  
pp. 362 ◽  
Author(s):  
Youngsoon Kim ◽  
Jie Hao ◽  
Yadu Gautam ◽  
Tesfaye B. Mersha ◽  
Mingon Kang
Keyword(s):  
Network Analysis ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Differential Gene ◽  
Gene Regulatory

DiffGRN: differential gene regulatory network analysis

2018 ◽  
Vol 20 (4) ◽  
pp. 362
Author(s):  
Mingon Kang ◽  
Jie Hao ◽  
Tesfaye B. Mersha ◽  
Yadu Gautam ◽  
Youngsoon Kim
Keyword(s):  
Network Analysis ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Differential Gene ◽  
Gene Regulatory

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

scholarly journals Correction: Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

2019 ◽  
Vol 79 (8) ◽  
pp. 2084-2084
Author(s):  
Camila M. Lopes-Ramos ◽  
Marieke L. Kuijjer ◽  
Shuji Ogino ◽  
Charles S. Fuchs ◽  
Dawn L. DeMeo ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Network Analysis ◽  
Drug Metabolism ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Cancer Drug ◽  
Gene Regulatory

Identification of Hub Molecules of FUS-ALA by Bayesian Gene Regulatory Network Analysis of iPSC Model: iBRN

2020 ◽  
Author(s):  
Masahiro Nogami ◽  
Mitsuru Ishikawa ◽  
Atsushi Doi ◽  
Osamu Sano ◽  
Takefumi Sone ◽  
...  
Keyword(s):  
Network Analysis ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Regulatory

scholarly journals Integrative Gene Regulatory Network Analysis Reveals Light-Induced Regional Gene Expression Phase Shift Programs in the Mouse Suprachiasmatic Nucleus

PLoS ONE ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. e37833 ◽  
Author(s):  
Haisun Zhu ◽  
Rajanikanth Vadigepalli ◽  
Rachel Rafferty ◽  
Gregory E. Gonye ◽  
David R. Weaver ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Phase Shift ◽  
Gene Regulatory Network ◽  
Suprachiasmatic Nucleus ◽  
Regulatory Network ◽  
Gene Regulatory ◽  
Regional Gene

miRNA mediated gene regulatory network analysis of Cichorium intybus (chicory)

Agri Gene ◽  
2017 ◽  
Vol 3 ◽  
pp. 37-45 ◽  
Author(s):  
Swati Srivastava ◽  
Noopur Singh ◽  
Gaurava Srivastava ◽  
Ashok Sharma
Keyword(s):  
Network Analysis ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Cichorium Intybus ◽  
Gene Regulatory

scholarly journals IGFBP2 is a Potential Master-Regulator Driving Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme

2020 ◽  
Author(s):  
Manasa KP ◽  
Darius Wlochowitz ◽  
Edgar Wingender ◽  
Tim Beißbarth ◽  
ALEXANDER KEL
Keyword(s):  
Transcription Factors ◽  
Glioblastoma Multiforme ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Epithelial To Mesenchymal Transition ◽  
Mesenchymal Transition ◽  
Short Survival ◽  
Tumor Invasiveness ◽  
Gene Regulatory

Only two percent of Glioblastoma multiforme (GBM) patients respond to standard care and survive beyond 36 months (long-term survivors, LTS) while the majority survives less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that co-occur with aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the GO-categories “epithelial to mesenchymal transition” and “response to hypoxia”. In this paper we applied upstream analysis approach which involves state-of-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Transcription factors associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG and seven others were found enriched in regulatory regions of the dysregulated genes. Based on network analysis, we propose novel gene regulatory network regulated by five master regulators – IGFBP2, VEGFA, VEGF165, PDGFA, AEBP1 and OSMR which can potentially act as therapeutic targets for enhancing GBM prognosis. Critical analysis of this gene regulatory network gives insights on mechanism of gene regulation by IGFBP2 via several transcription factors including the key molecule of GBM tumor invasiveness and progression FRA-1. All the observations are validated in independent cohorts and their impact on overall is studied on TCGA-GBM RNA seq data.

Sign in / Sign up

Export Citation Format

Share Document