@article{Nepomuceno-Chamorro2020,

title = {Using Prior Knowledge in the Inference of Gene Association Networks},

author = {I A Nepomuceno-Chamorro and B Vega-Márquez and J L Galván-Rojas and C Rubio-Escudero and J A Nepomuceno},

doi = {10.1007/s10489-020-01705-4},

year  = {2020},

date = {2020-03-31},

journal = {Applied Intelligence},

note = {JCR (2018): 2.882},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{B2020,

title = {Deep Learning Techniques to Improve the Performance of Olive Oil Classification},

author = {B Vega-Márquez and I Nepomuceno-Chamorro and N Jurado-Campos and C Rubio-Escudero},

doi = {10.3389/fchem.2019.00929},

year  = {2020},

date = {2020-01-17},

journal = {Frontiers in Chemistry},

volume = {7},

pages = {929},

note = {JCR (2018): 3.782},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nepomuceno2018,

title = {Pairwise gene GO-based measures for biclustering of high-dimensional expression data},

author = {J A Nepomuceno and A Troncoso and I A Nepomuceno-Chamorro and J S Aguilar-Ruiz},

doi = {10.1186/s13040-018-0165-9},

year  = {2018},

date = {2018-03-27},

journal = {BioData mining},

volume = {11},

number = {4},

note = {JCR(2018): 2.301},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ntna18,

title = {Pairwise gene GO-based measures for biclustering of high-dimensional expression data},

author = {J Nepomuceno and A Troncoso-Lora and I A Nepomuceno-Chamorro and J Aguilar-Ruiz},

doi = {https://doi.org/10.1186/s13040-018-0165-9},

year  = {2018},

date = {2018-01-01},

journal = {Biodata Mining},

volume = {11},

number = {1},

pages = {1-4},

note = {JCR(2016): 1,57},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rvrnhp17,

title = {Data mining techniques applied to hydrogen lactose breath test},

author = {C Rubio-Escudero and J Valverde-Fernandez and A Rodríguez-Herrera and I Nepomuceno-Chamorro and Y Hernández-Mendoza and B Pontes-Balanza},

doi = {10.1371/journal.pone.0170385},

year  = {2017},

date = {2017-01-01},

journal = {PLOS ONE},

volume = {12},

number = {1},

note = {IF: 3.057, Q1 11/56},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mgnr17,

title = {Machine learning techniques to discover genes with Potential Prognosis Role in Alzheimer's Disease using different biological sources},

author = {M Martínez-Ballesteros and J M García-Heredia and I Nepomuceno-Chamorro and J C Riquelme},

doi = {10.1016/j.inffus.2016.11.005},

year  = {2017},

date = {2017-01-01},

journal = {Information Fusion},

volume = {36},

pages = {114-129},

note = {JCR (2015): 4.353, Q1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{n16,

title = {Model tree to improve the inference of gene association networks},

author = {I A Nepomuceno-Chamorro},

doi = {10.3233/AIC-160700},

year  = {2016},

date = {2016-01-01},

journal = {AI Communications},

volume = {29},

number = {4},

pages = {547-549},

note = {JCR (2015): 0.364 Q4 126/134 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nma15,

title = {Building transcriptional association networks in Cytoscape with RegNetC},

author = {I A Nepomuceno-Chamorro and A E Márquez and J S Aguilar Ruiz},

doi = {10.1109/TCBB.2014.2385702},

year  = {2015},

date = {2015-01-01},

journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},

volume = {12},

number = {4},

pages = {823-4},

note = {IF: 1.609 Q1 26/123 STATISTICS & PROBABILITY, Q2 32/101 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ntna15,

title = {Integrating biological knowledge based on functional annotations for biclustering of gene expression data},

author = {J A Nepomuceno and A Troncoso-Lora and I A Nepomuceno-Chamorro and J Aguilar-Ruiz},

doi = {10.1016/j.cmpb.2015.02.010},

year  = {2015},

date = {2015-01-01},

journal = {Computer Methods and Programs in Biomedicine},

volume = {119},

number = {3},

pages = {163-180},

note = {JCR (2015): 1.862 Q1 16/105 Computer Science, Theory and Methods},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mnr14,

title = {Discovering gene association networks by multi-objective evolutionary quantitative association rules},

author = {M Martínez-Ballesteros and I A Nepomuceno-Chamorro and J C Riquelme},

doi = {10.1016/J.JCSS.2013.03.010},

year  = {2014},

date = {2014-01-01},

journal = {Journal of Computer and Systems Sciences},

volume = {89},

number = {1},

pages = {118-136},

note = {JCR (2014): , Q2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rnnjgi,

title = {Transcriptional response to cardiac injury in the zebrafish: systematic identification of genes with highly concordant activity across in vivo models.},

author = {S Rodius and P V Nazarov and I A Nepomuceno-Chamorro and C Jeanty and J M González-Rosa and M Ibberson and Benites Costa da R.M. and Xenarios I. and Mercader N. and Azuaje F.},

doi = {10.1186/1471-2164-15-852},

year  = {2014},

date = {2014-01-01},

journal = {BMC Genomics},

volume = {15},

number = {896},

pages = {852-867},

note = {IF: 26/163 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jgmprnppga14,

title = {Data mining tools for predicting the risk of toxicity in prostate cancer patients treated with radiation therapy},

author = {R Jose and J L Lopez Guerra and R Matute and B Pontes and C Rubio and I Nepomuceno and F Puebla and J M Praena-Fernandez and M J Ortiz Gordillo and I Azinovic},

doi = {10.1016/S0167-8140(15)30841-0},

year  = {2014},

date = {2014-01-01},

journal = {Radiotherapy Oncology},

volume = {111},

number = {1},

pages = {S30},

note = {IF: 4.317, Q1 11/125},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nadnmad11,

title = {Prognostic transcriptional association networks: A new supervised approach based on regression trees},

author = {I A Nepomuceno-Chamorro and F Azuaje and Y Devaux and P V Nazarov and A Muller and J S Aguilar-Ruiz and Wagner Dr.},

doi = {10.1093/bioinformatics/btq645},

year  = {2011},

date = {2011-01-01},

journal = {Bioinformatics},

volume = {27},

number = {2},

pages = {252-258},

note = {IF: 5.468 15/158 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ardn11,

title = {CARGENE: Characterization of Set of Genes based on Metabolic Pathway Analysis},

author = {J S Aguilar-Ruiz and Rodr D í and N Diaz-Diaz and I Nepomuceno},

doi = {10.1504/IJDMB.2011.043033},

year  = {2011},

date = {2011-01-01},

journal = {International Journal of Data Mining and Bioinformatics},

volume = {5},

number = {5},

pages = {558-573},

note = {IF: 0.429 Q4 44/46 MATHEMATICAL & COMPUTATIONAL BIOLOGY},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{naj10,

title = {Inferring gene regression networks with model trees},

author = {I A Nepomuceno-Chamorro and J S Aguilar-Ruiz and Riquelme Jc.},

doi = {10.1186/1471-2105-11-517},

year  = {2010},

date = {2010-01-01},

journal = {BMC Bioinformatics},

volume = {11},

pages = {517-529},

note = {IF: Q1 4/37 MATHEMATICAL & COMPUTATIONAL BIOLOGY, Q2 57/158 BIOTECHNOLOGY & APPLIED MICROBIOLOGY},

keywords = {},

pubstate = {published},

tppubtype = {article}

}