Publications
2020 |
Delgado, F. M.; Gómez-Vela, F.; Divina, F.; García-Torres, M.; Rodríguez-Baena, D. Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks Journal Article In: Genes, vol. 11, no. 7, pp. 831, 2020, ISSN: 2073-4425. Abstract | Links | BibTeX | Tags: Data Mining, Gene Network, murine coronavirus, Systems biology, Viral infection @article{Delgado2020,Gene networks have arisen as a promising tool in the comprehensive modeling and analysis of complex diseases. Particularly in viral infections, the understanding of the host-pathogen mechanisms, and the immune response to these, is considered a major goal for the rational design of appropriate therapies. For this reason, the use of gene networks may well encourage therapy-associated research in the context of the coronavirus pandemic, orchestrating experimental scrutiny and reducing costs. In this work, gene co-expression networks were reconstructed from RNA-Seq expression data with the aim of analyzing the time-resolved effects of gene Ly6E in the immune response against the coronavirus responsible for murine hepatitis (MHV). Through the integration of differential expression analyses and reconstructed networks exploration, significant differences in the immune response to virus were observed in Ly6E?HSC compared to wild type animals. Results show that Ly6E ablation at hematopoietic stem cells (HSCs) leads to a progressive impaired immune response in both liver and spleen. Specifically, depletion of the normal leukocyte mediated immunity and chemokine signaling is observed in the liver of Ly6E?HSC mice. On the other hand, the immune response in the spleen, which seemed to be mediated by an intense chromatin activity in the normal situation, is replaced by ECM remodeling in Ly6E?HSC mice. These findings, which require further experimental characterization, could be extrapolated to other coronaviruses and motivate the efforts towards novel antiviral approaches. |
2019 |
Gómez-Vela, F.; Delgado, F. M.; Rodríguez-Baena, D.; García-Torres, M.; Divina, F. Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks Journal Article In: Entropy, vol. 21, no. 12, pp. 1139, 2019. Abstract | Links | BibTeX | Tags: Ensemble networks, Gene Network @article{Gómez-Vela2019,Gene networks have become a powerful tool in the comprehensive analysis of gene expression. Due to the increasing amount of available data, computational methods for networks generation must deal with the so-called curse of dimensionality in the quest for the reliability of the obtained results. In this context, ensemble strategies have significantly improved the precision of results by combining different measures or methods. On the other hand, structure optimization techniques are also important in the reduction of the size of the networks, not only improving their topology but also keeping a positive prediction ratio. In this work, we present Ensemble and Greedy networks (EnGNet), a novel two-step method for gene networks inference. First, EnGNet uses an ensemble strategy for co-expression networks generation. Second, a greedy algorithm optimizes both the size and the topological features of the network. Not only do achieved results show that this method is able to obtain reliable networks, but also that it significantly improves topological features. Moreover, the usefulness of the method is proven by an application to a human dataset on post-traumatic stress disorder, revealing an innate immunity-mediated response to this pathology. These results are indicative of the method’s potential in the field of biomarkers discovery and characterization. |
Delgado, F. M.; Gómez-Vela, F. Computational methods for Gene Regulatory Networks reconstruction and analysis: A review Journal Article In: Artificial Intelligence in Medicine, vol. 95, pp. 133-145, 2019, ISSN: 0933-3657. Abstract | Links | BibTeX | Tags: Gene Network, Systems biology @article{Delgado2019,In the recent years, the vast amount of genetic information generated by new-generation approaches, have led to the need of new data handling methods. The integrative analysis of diverse-nature gene information could provide a much-sought overview to study complex biological systems and processes. In this sense, Gene Regulatory Networks (GRN) arise as an increasingly-promising tool for the modelling and analysis of biological processes. This review is an attempt to summarize the state of the art in the field of GRNs. Essential points in the field are addressed, thereof: (a) the type of data used for network generation, (b) machine learning methods and tools used for network generation, (c) model optimization and (d) computational approaches used for network validation. This survey is intended to provide an overview of the subject for readers to improve their knowledge in the field of GRN for future research. |
2018 |
Gómez-Vela, F.; Rodríguez-Baena, D.; Vázquez-Noguera, J. L. Structure Optimization for Large Gene Networks Based on Greedy Strategy Journal Article In: Computational and Mathematical Methods in Medicine, vol. 2018, 2018. Abstract | Links | BibTeX | Tags: Gene Network, Soft Computing @article{Gómez-Vela2018,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. |
Díaz-Montaña, J. J.; Gómez-Vela, F.; Díaz-Díaz, N. GNC–app: A new Cytoscape app to rate gene networks biological coherence using gene–gene indirect relationships Journal Article In: Biosystems, vol. 166, pp. 61-65, 2018, ISSN: 0303-2647. Abstract | Links | BibTeX | Tags: Cytoscape, Gene Network @article{Díaz-Montaña2018,Motivation Gene networks are currently considered a powerful tool to model biological processes in the Bioinformatics field. A number of approaches to infer gene networks and various software tools to handle them in a visual simplified way have been developed recently. However, there is still a need to assess the inferred networks in order to prove their relevance. Results In this paper, we present the new GNC-app for Cytoscape. GNC-app implements the GNC methodology for assessing the biological coherence of gene association networks and integrates it into Cytoscape. Implemented de novo, GNC-app significantly improves the performance of the original algorithm in order to be able to analyse large gene networks more efficiently. It has also been integrated in Cytoscape to increase the tool accessibility for non-technical users and facilitate the visual analysis of the results. This integration allows the user to analyse not only the global biological coherence of the network, but also the biological coherence at the gene–gene relationship level. It also allows the user to leverage Cytoscape capabilities as well as its rich ecosystem of apps to perform further analyses and visualizations of the network using such data. Availability The GNC-app is freely available at the official Cytoscape app store: http://apps.cytoscape.org/apps/gnc. |
2017 |
Díaz-Montaña, J. J.; Díaz-Díaz, N.; Gómez-Vela, F. GFD-Net: A novel semantic similarity methodology for the analysis of gene networks Journal Article In: Journal of Biomedical Informatics, vol. 68, pp. 71-82, 2017, ISSN: 1532-0464. Abstract | Links | BibTeX | Tags: Gene Network @article{Díaz-Montaña2017,Since the popularization of biological network inference methods, it has become crucial to create methods to validate the resulting models. Here we present GFD-Net, the first methodology that applies the concept of semantic similarity to gene network analysis. GFD-Net combines the concept of semantic similarity with the use of gene network topology to analyze the functional dissimilarity of gene networks based on Gene Ontology (GO). The main innovation of GFD-Net lies in the way that semantic similarity is used to analyze gene networks taking into account the network topology. GFD-Net selects a functionality for each gene (specified by a GO term), weights each edge according to the dissimilarity between the nodes at its ends and calculates a quantitative measure of the network functional dissimilarity, i.e. a quantitative value of the degree of dissimilarity between the connected genes. The robustness of GFD-Net as a gene network validation tool was demonstrated by performing a ROC analysis on several network repositories. Furthermore, a well-known network was analyzed showing that GFD-Net can also be used to infer knowledge. The relevance of GFD-Net becomes more evident in Section “GFD-Net applied to the study of human diseases†where an example of how GFD-Net can be applied to the study of human diseases is presented. GFD-Net is available as an open-source Cytoscape app which offers a user-friendly interface to configure and execute the algorithm as well as the ability to visualize and interact with the results(http://apps.cytoscape.org/apps/gfdnet). |
2016 |
Gómez-Vela, F.; Barranco, C. D.; Díaz-Díaz, N. Incorporating biological knowledge for construction of fuzzy networks of gene associations Journal Article In: Applied Soft Computing, vol. 42, pp. 144-155, 2016, ISSN: 1568-4946. Abstract | Links | BibTeX | Tags: Gene Network @article{Gómez-Vela2016,Gene association networks have become one of the most important approaches to modelling of biological processes by means of gene expression data. According to the literature, co-expression-based methods are the main approaches to identification of gene association networks because such methods can identify gene expression patterns in a dataset and can determine relations among genes. These methods usually have two fundamental drawbacks. Firstly, they are dependent on quality of the input dataset for construction of reliable models because of the sensitivity to data noise. Secondly, these methods require that the user select a threshold to determine whether a relation is biologically relevant. Due to these shortcomings, such methods may ignore some relevant information. We present a novel fuzzy approach named FyNE (Fuzzy NEtworks) for modelling of gene association networks. FyNE has two fundamental features. Firstly, it can deal with data noise using a fuzzy-set-based protocol. Secondly, the proposed approach can incorporate prior biological knowledge into the modelling phase, through a fuzzy aggregation function. These features help to gain some insights into doubtful gene relations. The performance of FyNE was tested in four different experiments. Firstly, the improvement offered by FyNE over the results of a co-expression-based method in terms of identification of gene networks was demonstrated on different datasets from different organisms. Secondly, the results produced by FyNE showed its low sensitivity to noise data in a randomness experiment. Additionally, FyNE could infer gene networks with a biological structure in a topological analysis. Finally, the validity of our proposed method was confirmed by comparing its performance with that of some representative methods for identification of gene networks |
2015 |
Gómez-Vela, F.; Lagares, J. A.; Díaz-Díaz, N. Gene network coherence based on prior knowledge using direct and indirect relationships Journal Article In: Computational Biology and Chemistry, vol. 56, pp. 142-151, 2015, ISSN: 1476-9271. Abstract | Links | BibTeX | Tags: Biological knowledge, Gene Network @article{Gómez-Vela2015,Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships. We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/. The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation. |
2011 |
Gómez-Vela, F.; Martínez-Álvarez, F.; Barranco, C. D.; Díaz-Díaz, N.; Rodríguez-Baena, D.; Aguilar-Ruiz, J. Pattern Recognition in Biological Time Series Journal Article In: Advances in Artificial Intelligence, pp. 164-172, 2011, ISBN: 978-3-642-25274-7. Abstract | Links | BibTeX | Tags: Biclustering, Clustering, Gene Network @article{Gómez-Vela2011b,Knowledge extraction from gene expression data has been one of the main challenges in the bioinformatics field during the last few years. In this context, a particular kind of data, data retrieved in a temporal basis (also known as time series), provide information about the way a gene can be expressed during time. This work presents an exhaustive analysis of last proposals in this area, particularly focusing on those proposals using non--supervised machine learning techniques (i.e. clustering, biclustering and regulatory networks) to find relevant patterns in gene expression. |
Gómez-Vela, F.; Díaz-Díaz, N.; Aguilar-Ruiz, J. Gene Networks Validation based on Metabolic Pathways Conference 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering, 2011. Abstract | Links | BibTeX | Tags: Gene Network @conference{Gómez-Vela2011,In the last few years, DNA microarray technology has attained a very important role in biological and biomedical research. It enables analyzing the relations among thousands of genes simultaneously, generating huge amounts of data. The gene networks represent, in a graph data structure, genes or gene products and the functional relationships between them. These models have been fully used in Bioinformatics because they provide an easy way to understand gene expression regulation. Nowadays, a lot of gene network algorithms have been developed as knowledge extraction techniques. A very important task in all these studies is to assure the network models reliability in order to prove that the methods used are precise. This validation process can be carried out by using the inherent information of the input data or by using public biological knowledge. In this last case, these sources of information provide a great opportunity of verifying the biological soundness of the generated networks. In this work, authors present a gene network validation methodology based on the information stored in Kegg database. With this aim, a complete Kegg pathway conversion to gene network is presented, and a global and functional validation process is proposed, where the whole metabolical information stored in Kegg is used at the same time. |
Díaz-Díaz, N.; Gómez-Vela, F.; Rodríguez-Baena, D.; Aguilar-Ruiz, J. Gene Regulatory Networks Validation Framework Based in KEGG Conference Hybrid Artificial Intelligent Systems, 2011, ISBN: 978-3-642-21222-2. Abstract | Links | BibTeX | Tags: Biological knowledge, Gene Network @conference{Díaz-Díaz2011,In the last few years, DNA microarray technology has attained a very important role in biological and biomedical research. It enables analyzing the relations among thousands of genes simultaneously, generating huge amounts of data. The gene regulatory networks represent, in a graph data structure, genes or gene products and the functional relationships between them. These models have been fully used in Bioinformatics because they provide an easy way to understand gene expression regulation. |
2007 |
Nepomuceno-Chamorro, I. A.; Aguilar-Ruiz, J.; Díaz-Díaz, N.; Rodríguez-Baena, D.; García, J. A Deterministic Model to Infer Gene Networks from Microarray Data Conference Intelligent Data Engineering and Automated Learning - IDEAL 2007, 2007, ISBN: 978-3-540-77226-2. Abstract | Links | BibTeX | Tags: Gene Network @conference{Nepomuceno-Chamorro2007,Microarray experiments help researches to construct the structure of gene regulatory networks, i.e., networks representing relationships among different genes. Filter and knowledge extraction processes are necessary in order to handle the huge amount of data produced by microarray technologies. We propose regression trees techniques as a method to identify gene networks. Regression trees are a very useful technique to estimate the numerical values for the target outputs. They are very often more precise than linear regression models because they can adjust different linear regressions to separate areas of the search space. In our approach, we generate a single regression tree for each genes from a set of genes, taking as input the remaining genes, to finally build a graph from all the relationships among output and input genes. In this paper, we will simplify the approach by setting an only seed, the gene ARN1, and building the graph around it. The final model might gives some clues to understand the dynamics, the regulation or the topology of the gene network from one (or several) seeds, since it gathers relevant genes with accurate connections. The performance of our approach is experimentally tested on the yeast Saccharomyces cerevisiae dataset (Rosetta compendium). |