Gene regulatory network from microarray data using dynamic neural fuzzy approach

Background Cell cycle is an important clue to unravel the mechanism of cancer cells. Recently, expression profiles of cDNA microarray data of Cancer cell cycle are available for the information of dynamic interactions among Cancer cell cycle related genes. Therefore, it is more appealing to construct a dynamic model for gene regulatory network of Cancer cell cycle to gain more insight into the infrastructure of gene regulatory mechanism of cancer cell via microarray data. Results Based on the gene regulatory dynamic model and microarray data, we construct the whole dynamic gene regulatory network of Cancer cell cycle. In this study, we trace back upstream regulatory genes of a target gene to infer the regulatory pathways of the gene network by maximum likelihood estimation method. Finally, based on the dynamic regulatory network, we analyze the regulatory abilities and sensitivities of regulatory genes to clarify their roles in the mechanism of Cancer cell cycle. Conclusions Our study presents a systematically iterative approach to discern and characterize the transcriptional regulatory network in Hela cell cycle from the raw expression profiles. The transcription regulatory network in Hela cell cycle can also be confirmed by some experimental reviews. Based on our study and some literature reviews, we can predict and clarify the E2F target genes in G1/S phase, which are crucial for regulating cell cycle progression and tumorigenesis. From the results of the network construction and literature confirmation, we infer that MCM4, MCM5, CDC6, CDC25A, UNG and E2F2 are E2F target genes in Hela cell cycle.

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Identifying the gene regulatory network of systems inflammation in humans by system dynamic model via microarray data and database mining

Systems Immunology and Infection Microbiology ◽

10.1016/b978-0-12-816983-4.00008-0 ◽

2021 ◽

pp. 23-86

Author(s):

Bor-Sen Chen

Keyword(s):

Dynamic Model ◽

Gene Regulatory Network ◽

Microarray Data ◽

Regulatory Network ◽

System Dynamic ◽

System Dynamic Model ◽

Database Mining ◽

Gene Regulatory

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Research Trends in Microarray Data Analysis: Modelling Gene Regulatory Network by Integrating Transcription Factors Data

Scientific Research Journal ◽

10.24191/srj.v12i1.5437 ◽

2015 ◽

Vol 12 (1) ◽

pp. 39

Author(s):

Farzana Kabir Ahmad ◽

Siti Sakira Kamaruddin

Keyword(s):

Bayesian Network ◽

Gene Regulatory Network ◽

Microarray Data ◽

False Positive ◽

Regulatory Network ◽

Structure Learning ◽

Microarray Data Analysis ◽

Biological Knowledge ◽

Pharmaceutical Industries ◽

Gene Regulatory

The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence, this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network.

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