A Heuristic Gene Regulatory Networks Model for Cardiac Function and Pathology

Zarnegar, Armita; Vamplew, Peter; Stranieri, Andrew; Jelinek, Herbert F.

doi:10.22489/cinc.2016.101-135

Cited by 1 publication

(1 citation statement)

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“…GRN inference is improved when clustering process is introduced in edge selection process. A novel technique for discovery of gene regulatory network is proposed [10] in which discovery process is integrated into heuristic information. To construct large scale gene regulatory networks a dynamic multi-agent genetic algorithm is represented [11], which is based on FCM.…”

Section: Related Workmentioning

confidence: 99%

A Simple Approach for Representation of Gene Regulatory Networks (GRN)

Haq¹,

Ferzund²,

Hussain³

2018

ijacsa

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Gene expressions are controlled by a series of processes known as Gene Regulation, and their abstract mapping is represented by Gene Regulatory Network (GRN) which is a descriptive model of gene interactions. Reverse engineering GRNs can reveal the complexity of gene interactions whose comprehension can lead to several other details. RNA-seq data provides better measurement of gene expressions; however it is difficult to infer GRNs using it because of its discreteness. Multiple other methods have already been proposed to infer GRN using RNA-seq data, but these methodologies are difficult to grasp. In this paper, a simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network. The obtained results show that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.

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Section: Related Workmentioning

confidence: 99%