Incorporating biological knowledge for construction of fuzzy networks of gene associations

Gómez-Vela, Francisco; Díaz–Díaz, Norberto

doi:10.1016/j.asoc.2016.01.014

Cited by 11 publications

(16 citation statements)

References 61 publications

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“…GeSOp is a novel method for large gene networks topology optimization. The method uses undirected influence networks since they represent the highest level of abstraction in the gene networks as was discussed in [ 3 ]. Due to this, our method can be applied for a larger number of networks since almost any gene network can be transformed into a nondirected influence network.…”

Section: Methodsmentioning

confidence: 99%

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Structure Optimization for Large Gene Networks Based on Greedy Strategy

Gómez-Vela

Rodríguez–Baena

Vázquez-Noguera

2018

Computational and Mathematical Methods in Medicine

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In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network's structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.

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Section: Methodsmentioning

confidence: 99%

“…Gene networks are usually inferred from gene expression data and have been widely used to model gene relationships in a biological process [ 3 ]. In the last decade, many computational approaches have been proposed for the reverse engineering of gene networks [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Structure Optimization for Large Gene Networks Based on Greedy Strategy

Gómez-Vela

Rodríguez–Baena

Vázquez-Noguera

2018

Computational and Mathematical Methods in Medicine

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“…Note that co-expressions between genes may be either positive or negative, so these thresholds are expressed as absolute values. These thresholds were defined in accordance to statistical standards [6,46,47]. Thr.…”

Section: Network Inferencementioning

confidence: 99%

“…According to the different works in the literature [1,6], GN inference algorithms lie under four main categories: co-expression, boolean networks, differential equation-based and Bayesian networks. Within this classification, co-expression networks, which are based on information theory algorithms, arise as a significantly relevant approach due to their computational simplicity and extensive use in the literature [1,7].…”

Section: Introductionmentioning

confidence: 99%

“…Ordinarily, GN inference algorithms take gene expression datasets, e.g., microarrays or RNA-Seq, as input for the generation of the gene-gene interactions [6,7,15]. These datasets have been massively generated over the last decade for the study of some type of biological process or specific disease [16], allowing the identification of relationships between DNA, RNA, proteins and other gene products.…”

Section: Introductionmentioning

confidence: 99%

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

Delgado-Chaves

Gómez-Vela

García-Torres

et al. 2019

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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.

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