Differential regulation enrichment analysis via the integration of transcriptional regulatory network and gene expression data

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).

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Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information

BMC Bioinformatics ◽

10.1186/1471-2105-9-467 ◽

2008 ◽

Vol 9 (1) ◽

pp. 467 ◽

Cited By ~ 43

Author(s):

Weijun Luo ◽

Kurt D Hankenson ◽

Peter J Woolf

Keyword(s):

Gene Expression ◽

Mutual Information ◽

Gene Expression Data ◽

High Throughput ◽

Regulatory Networks ◽

Expression Data ◽

Transcriptional Regulatory Networks ◽

Transcriptional Regulatory ◽

High Throughput Gene Expression

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Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

Cancer Informatics ◽

10.4137/cin.s13882 ◽

2014 ◽

Vol 13s1 ◽

pp. CIN.S13882 ◽

Cited By ~ 4

Author(s):

Binghuang Cai ◽

Xia Jiang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Expression Data ◽

Lung Squamous Cell Carcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Expression Data ◽

Gene Set Enrichment ◽

Gene Set ◽

Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

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