- Title
- Integrating biological heuristics and gene expression data for gene regulatory network inference
- Creator
- Zarnegar, Armita; Jelinek, Herbert; Vamplew, Peter; Stranieri, Andrew
- Date
- 2019
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/170554
- Identifier
- vital:14196
- Identifier
-
https://doi.org/10.1145/3290688.3290741
- Identifier
- ISBN:978-1-4503-6603-8
- Abstract
- Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.
- Publisher
- Assoc Computing Machinery
- Relation
- 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-10
- Rights
- Copyright © 2019 Association for Computing Machinery.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Gene expression; Gene regulatory network; Hubs; Association function; Correlation function
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