Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Tong, DL, Boocock, DJ, Dhondalay, GKR, Lemetre, C and Ball, GR ORCID: https://orcid.org/0000-0001-5828-7129, 2014. Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas. PLoS ONE, 9 (7), e102483. ISSN 1932-6203

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Abstract

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI).
Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model.
Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen.
Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.

Item Type:	Journal article
Publication Title:	PLoS ONE
Creators:	Tong, D.L., Boocock, D.J., Dhondalay, G.K.R., Lemetre, C. and Ball, G.R.
Publisher:	Public Library of Science
Date:	2014
Volume:	9
Number:	7
ISSN:	1932-6203
Identifiers:	Number Type 10.1371/journal.pone.0102483 DOI
Rights:	Copyright: © 2014 Tong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	10 Feb 2016 15:16
Last Modified:	09 Jun 2017 13:59
URI:	https://irep.ntu.ac.uk/id/eprint/26929

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