Publications

@article{Delgado2020,

title = {Computational Analysis of the Global Effects of Ly6E in the Immune Response to Coronavirus Infection Using Gene Networks},

author = {F. M. Delgado and F. Gómez-Vela and F. Divina and M. García-Torres and D. Rodríguez-Baena},

url = {https://www.mdpi.com/2073-4425/11/7/831},

doi = {10.3390/genes11070831},

issn = {2073-4425},

year  = {2020},

date = {2020-07-21},

journal = {Genes},

volume = {11},

number = {7},

pages = {831},

abstract = {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.},

keywords = {Data Mining, Gene Network, murine coronavirus, Systems biology, Viral infection},

pubstate = {published},

tppubtype = {article}

}

@article{Delgado2019,

title = {Computational methods for Gene Regulatory Networks reconstruction and analysis: A review},

author = {F. M. Delgado and F. Gómez-Vela},

url = {http://www.sciencedirect.com/science/article/pii/S0933365718303865},

doi = {10.1016/j.artmed.2018.10.006},

issn = {0933-3657},

year  = {2019},

date = {2019-04-01},

journal = {Artificial Intelligence in Medicine},

volume = {95},

pages = {133-145},

abstract = {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.},

keywords = {Gene Network, Systems biology},

pubstate = {published},

tppubtype = {article}

}