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Open AccessArticle

Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach

1
Division of Computer Science, Pablo de Olavide University, 41013 Seville, Spain
2
Computer Engineer Department, Universidad Americana de Paraguay, Asuncion 1209, Paraguay
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2019, 10(12), 962; https://doi.org/10.3390/genes10120962
Received: 14 October 2019 / Revised: 28 October 2019 / Accepted: 31 October 2019 / Published: 22 November 2019
(This article belongs to the Special Issue Current Advances in Network Biology for Disease Understanding)
Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes N C K A P 1 L and D M D are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers. View Full-Text
Keywords: co-expression network; lung carcinoma; biomarker discovery; ensemble network; data mining; Bioinformatics co-expression network; lung carcinoma; biomarker discovery; ensemble network; data mining; Bioinformatics
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MDPI and ACS Style

Delgado-Chaves, F.M.; Gómez-Vela, F.; García-Torres, M.; Divina, F.; Vázquez Noguera, J.L. Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach. Genes 2019, 10, 962.

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