Publications

@article{Lasarte-Monterrubio2024-fh,

title = {Epidemiology, resistance genomics and susceptibility of  Acinetobacter species: results from the 2020 Spanish nationwide  surveillance study},

author = {Cristina Lasarte-Monterrubio and Paula Guijarro-Sánchez and Isaac Alonso-Garcia and Michelle Outeda and Romina Maceiras and Lucia González-Pinto and Marta Martínez-Guitián and Carlos Fernández-Lozano and Juan Carlos Vázquez-Ucha and German Bou and Jorge Arca-Suárez and Alejandro Beceiro},

doi = {10.2807/1560-7917.ES.2024.29.15.2300352},

year  = {2024},

date = {2024-04-01},

urldate = {2024-04-01},

journal = {Euro Surveillance},

volume = {29},

number = {15},

address = {Sweden},

abstract = {BackgroundAs increasing antibiotic resistance in Acinetobacter 

 baumannii poses a global healthcare challenge, understanding its 

 evolution is crucial for effective control strategies.AimWe aimed 

 to evaluate the epidemiology, antimicrobial susceptibility and 

 main resistance mechanisms of Acinetobacter spp. in Spain in 

 2020, and to explore temporal trends of A. baumannii.MethodsWe 

 collected 199 single-patient Acinetobacter spp. clinical isolates 

 in 2020 from 18 Spanish tertiary hospitals. Minimum inhibitory 

 concentrations (MICs) for nine antimicrobials were determined. 

 Short-read sequencing was performed for all isolates, and 

 targeted long-read sequencing for A. baumannii. Resistance 

 mechanisms, phylogenetics and clonality were assessed. Findings 

 on resistance rates and infection types were compared with data 

 from 2000 and 2010.ResultsCefiderocol and colistin exhibited the 

 highest activity against A. baumannii, although colistin 

 susceptibility has significantly declined over 2 decades. A. 

 non-baumannii strains were highly susceptible to most tested 

 antibiotics. Of the A. baumannii isolates, 47.5% (56/118) were 

 multidrug-resistant (MDR). Phylogeny and clonal relationship 

 analysis of A. baumannii revealed five prevalent international clones, notably IC2 (ST},

note = {Q1, D1, 5/96 INF-DIS, 19.0 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carracedo-Reboredo2024,

title = {MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products},

author = {Paula Carracedo-Reboredo and Eider Aranzamendi and Shan He and Sonia Arrasate and Cristian R. Munteanu and Carlos Fernandez-Lozano and Nuria Sotomayor and Esther Lete and Humberto González-Díaz},

doi = {10.1186/s13321-024-00802-7},

issn = {1758-2946},

year  = {2024},

date = {2024-01-23},

urldate = {2024-01-23},

journal = {Journal of Cheminformatics},

volume = {16},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for different enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difficult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R<jats:sup>2</jats:sup> = 0.96 overall for training and validation series. It involved a Monte Carlo sampling of > 100,000 pairs of query and reference reactions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived <jats:italic>N</jats:italic>-triflylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo">https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo</jats:ext-link>. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.</jats:p>},

note = {Q1, D1, 11/158 CS-IS, 8.6 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vibes2023,

title = {VIBES: a consensus subtyping of the vaginal microbiota reveals novel classification criteria},

author = {D. Fernández-Edreira and J. Liñares-Blanco and P. V.-del-Río and C. Fernandez-Lozano},

editor = {Elsevier},

url = {https://www.csbj.org/article/S2001-0370(23)00465-8/fulltext},

doi = {https://doi.org/10.1016/j.csbj.2023.11.050},

issn = {2001-0370},

year  = {2023},

date = {2023-11-30},

urldate = {2023-11-30},

journal = {Computational and Structural Biotechnology Journal},

volume = {23},

pages = {148 - 156},

abstract = {This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.},

note = {Q1, 60/285 BIO-MB, 6 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{GOLOB2023101350,

title = {Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research},

author = {Jonathan L. Golob and Tomiko T. Oskotsky and Alice S. Tang and Alennie Roldan and Verena Chung and Connie W. Y. Ha and Ronald J. Wong and Kaitlin J. Flynn and Antonio Parraga-Leo and Camilla Wibrand and Samuel S. Minot and Boris Oskotsky and Gaia Andreoletti and Idit Kosti and Julie Bletz and Amber Nelson and Jifan Gao and Zhoujingpeng Wei and Guanhua Chen and Zheng-Zheng Tang and Pierfrancesco Novielli and Donato Romano and Ester Pantaleo and Nicola Amoroso and Alfonso Monaco and Mirco Vacca and Maria De Angelis and Roberto Bellotti and Sabina Tangaro and Abigail Kuntzleman and Isaac Bigcraft and Stephen Techtmann and Daehun Bae and Eunyoung Kim and Jongbum Jeon and Soobok Joe and Kevin R. Theis and Sherrianne Ng and Yun S. Lee and Patricia Diaz-Gimeno and Phillip R. Bennett and David A. MacIntyre and Gustavo Stolovitzky and Susan V. Lynch and Jake Albrecht and Nardhy Gomez-Lopez and Roberto Romero and David K. Stevenson and Nima Aghaeepour and Adi L. Tarca and James C. Costello and Marina Sirota},

url = {https://www.sciencedirect.com/science/article/pii/S2666379123005670},

doi = {https://doi.org/10.1016/j.xcrm.2023.101350},

issn = {2666-3791},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Cell Reports Medicine},

pages = {101350},

abstract = {Summary 

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.},

note = {Q1, D1, 5/135 MED, RES & EXP, 14.3 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{peerjcompesci2022,

title = {Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks},

author = {A. Carballal and J. P. Galego-Carro and N. Rodriguez-Fernandez and C. Fernandez-Lozano},

doi = {10.7717/peerj-cs.1185},

issn = {2376-5992},

year  = {2022},

date = {2022-12-01},

journal = {PeerJ Computer Science},

number = {8:e1185},

pages = {25},

publisher = {PeerJ},

note = {Q2, 48/110 CS-TH, 2.411 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{frontiers2022,

title = {Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes},

author = {J. Liñares-Blanco and C. Fernandez-Lozano and J. A. Seoane and G. Lopez-Campos},

doi = {10.3389/fmicb.2022.872671},

issn = {1664-302X},

year  = {2022},

date = {2022-05-01},

journal = {Frontiers in Microbiology},

volume = {13:872671},

pages = {11},

publisher = {Frontiers Publishing Group},

note = {Q1, 34/137 MICRO, 6.064 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{microbiolspectr2022,

title = {Carbapenem Resistance in Acinetobacter nosocomialis and Acinetobacter junii Conferred by Acquisition of blaOXA-24/40 and Genetic Characterization of the Transmission Mechanism between Acinetobacter Genomic Species},

author = {C. Lasarte-Monterrubio and P. Guijarro-Sánchez and A. Bellés and J. C. Vázquez-Ucha and J. Arca-Suárez and C. Fernandez-Lozano and G. Bou and A. Beceiro and Group},

doi = {10.1128/spectrum.02734-21},

issn = {2165-0497},

year  = {2022},

date = {2022-02-01},

journal = {Microbiology Spectrum},

volume = {10},

pages = {7},

publisher = {American Society for Microbiology},

note = {Q1, 20/137 MICRO, 9.043 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{csbj2021,

title = {A review on machine learning approaches and trends in drug discovery},

author = {P. * Carracedo-Reboredo and J. * Liñares-Blanco and N. Rodríguez-Fernández and F. Cedrón and F. J. Novoa and A. Carballal and V. Maojo and A. Pazos and C. Fernandez-Lozano},

doi = {10.1016/j.csbj.2021.08.011},

issn = {2001-0370},

year  = {2021},

date = {2021-08-01},

journal = {Computational and Structural Biotechnology Journal},

volume = {19},

pages = {21},

publisher = {Elsevier},

note = {Q1, 70/297 BIO-MB, 6.155 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{experSytems2021,

title = {Machine learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes},

author = {D. Fernández-Edreira and J. Liñares-Blanco and C. Fernandez-Lozano},

doi = {10.1016/j.eswa.2021.115648},

issn = {0957-4174},

year  = {2021},

date = {2021-07-01},

journal = {Expert Systems with Applications},

number = {115648},

pages = {13},

publisher = {Elsevier},

note = {Q1, <strong>D1</strong>, 8/87 OR-MS, 21/145 CS-AI, 8.665 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{peerjcompsci2021,

title = {Machine learning analysis of TCGA cancer data},

author = {J. Liñares-Blanco and A. Pazos and C. Fernandez-Lozano},

doi = {10.7717/peerj-cs.584},

issn = {2376-5992},

year  = {2021},

date = {2021-07-01},

journal = {PeerJ Computer Science},

number = {7:e584},

pages = {12},

publisher = {PeerJ},

note = {Q2, 48/110 CS-TH, 2.411 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{scientificreports2021,

title = {Random forest-based prediction of stroke outcome},

author = {Carlos Fernandez-Lozano and Pablo Hervella and Virginia Mato-Abad and Manuel Rodríguez-Yáñez and Sonia Suárez-Garaboa and Iria López-Dequidt and Ana Estany-Gestal and Tomás Sobrino and Francisco Campos and José Castillo and Santiago Rodríguez-Yáñez and Ramón Iglesias-Rey},

doi = {10.1038/s41598-021-89434-7},

issn = {2045-2322},

year  = {2021},

date = {2021-05-01},

journal = {Scientific Reports},

number = {11(10071)},

pages = {12},

publisher = {Nature Publishing Group},

note = {Q2, 19/74 MS, 4.997 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{peerjcompsci2020,

title = {Identification of predictive factors of the degree of adherence to the Mediterranean diet through machine-learning techniques},

author = {A. * Arceo-Vilas and C. * Fernandez-Lozano and S. Pita and S. Pértega-Díaz and A. Pazos},

doi = {10.7717/peerj-cs.287},

issn = {2376-5992},

year  = {2020},

date = {2020-07-01},

journal = {PeerJ Computer Science},

number = {6:e287},

pages = {18},

publisher = {PeerJ},

note = {Q1, 24/108 CS-TH, 3.091 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{docking2020,

title = {Molecular docking and machine learning analysis of Abemaciclib in colon cancer},

author = {Jose Liñares-Blanco and Cristian Robert Munteanu and Alejandro Pazos and Carlos Fernandez-Lozano},

doi = {10.1186/s12860-020-00295-w},

issn = {2661-8850},

year  = {2020},

date = {2020-07-01},

journal = {BMC Molecular and Cell Biology},

volume = {21},

number = {52},

pages = {18},

publisher = {BMC},

note = {Q4, 174/195 CE-BIO, 2.033 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carballal2020,

title = {Comparison of Outlier-Tolerant Models for Measuring Visual Complexity},

author = {Adrian Carballal and Carlos Fernandez-Lozano and Nereida Rodriguez-Fernandez and Iria Santos and Juan Romero},

doi = {10.3390/e22040488},

issn = {1099-4300},

year  = {2020},

date = {2020-04-01},

journal = {Entropy},

volume = {22(4)},

number = {488},

pages = {14},

publisher = {MDPI AG},

note = {Q2, 37/85 PH-MU, 2.524 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carracedo2020,

title = {MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry},

author = {P. Carracedo-Reboredo and R. Corona and M. Martinez-Nunes and C. Fernandez-Lozano and G. Tsiliki and H. Sarimveis and E. Aranzamendi and S. Arrasate and N. Sotomayor and E. Lete and C. R. Munteanu and H. González-Díaz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082979320&doi=10.2174%2f1568026620666191226092431&partnerID=40&md5=d147d004650405905219d1e1855f0bf9},

doi = {10.2174/1568026620666191226092431},

year  = {2020},

date = {2020-01-01},

journal = {Current Topics in Medicinal Chemistry},

note = {Q3, 35/63 CH-ME, 3.295 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{community2019,

title = {Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen},

author = {Menden, M.P. and Wang, D. and Mason, M.J. et al.},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85067453487&doi=10.1038%2fs41467-019-09799-2&origin=inward&txGid=293196aa7efa6448aa3ffd877f1b59fa},

doi = {https://doi.org/10.1038/s41467-019-09799-2},

isbn = {2041-1723},

year  = {2019},

date = {2019-06-19},

urldate = {2019-06-19},

journal = {Nature Communications},

volume = {10},

number = {2674},

note = {[Q1, D1, 6/71 MS, 12.121 IF]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rivero2019,

title = {Population subset selection for the use of a validation dataset for overfitting control in genetic programming},

author = {D. Rivero and E. Fernandez-Blanco and C. Fernandez-Lozano and A. Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070340585&doi=10.1080%2f0952813X.2019.1647562&partnerID=40&md5=c20dfee4d8c435cb6f6a620886500fd7},

doi = {10.1080/0952813X.2019.1647562},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Experimental and Theoretical Artificial Intelligence},

note = {Q3, 72/137 CS-AI, 2.039 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Piles2019,

title = {Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs},

author = {M. Piles and C. Fernandez-Lozano and M. Velasco-Galilea and O. González-Rodríguez and J. P. Sánchez and D. Torrallardona and M. Ballester and R. Quintanilla},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062882362&doi=10.1186%2fs12711-019-0453-y&partnerID=40&md5=ef184ffebc22679d183ad512bb8e28a6},

doi = {10.1186/s12711-019-0453-y},

year  = {2019},

date = {2019-01-01},

journal = {Genetics Selection Evolution},

volume = {51},

number = {1},

note = {Q1, <strong>D1</strong>, 4/63 AG-DAS, 3.95 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carballal2019,

title = {Transfer learning features for predicting aesthetics through a novel hybrid machine learning method},

author = {A. Carballal and C. Fernandez-Lozano and J. Heras and J. Romero},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061063491&doi=10.1007%2fs00521-019-04065-4&partnerID=40&md5=7a39a28331786fa14b2b770083204686},

doi = {10.1007/s00521-019-04065-4},

year  = {2019},

date = {2019-01-01},

journal = {Neural Computing and Applications},

note = {Q1, 23/137 CS-AI, 4.774 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano2019,

title = {Visual complexity modelling based on image features fusion of multiple kernels},

author = {C. * Fernandez-Lozano and A. * Carballal and P. Machado and A. Santos and J. Romero},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074152182&doi=10.7717%2fpeerj.7075&partnerID=40&md5=845e47d53bbcb7b0bc3216d1bc024220},

doi = {10.7717/peerj.7075},

year  = {2019},

date = {2019-01-01},

journal = {PeerJ},

volume = {2019},

number = {7},

note = {Q2, 32/71 MS, 2.379 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carballal2018,

title = {Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach},

author = {A. Carballal and C. Fernandez-Lozano and N. Rodriguez-Fernandez and L. Castro and A. Santos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060611392&doi=10.1155%2f2019%2f4659809&partnerID=40&md5=0cf5e3bb6de271a1efd9c844ee593db7},

doi = {10.1155/2019/4659809},

year  = {2018},

date = {2018-01-01},

journal = {Complexity},

volume = {2019},

note = {Q1, 21/105 M-IS, 22/64 MS, 2.591 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mata2019253,

title = {Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning},

author = {G. * Mata and M. * Radojević and C. * Fernandez-Lozano and I. Smal and N. Werij and M. Morales and E. Meijering and J. Rubio},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053509571&doi=10.1007%2fs12021-018-9399-4&partnerID=40&md5=d3eb4930a7b85a1aede0fe6a48413136},

doi = {10.1007/s12021-018-9399-4},

year  = {2018},

date = {2018-01-01},

journal = {Neuroinformatics},

volume = {17},

number = {2},

pages = {253-269},

note = {Q1, <strong>D1</strong>, 9/106 CS-IA, 5.127 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Blanco2018,

title = {Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection},

author = {J. Liñares-Blanco and A. B. Porto-Pazos and A. Pazos and C. Fernandez-Lozano},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055465334&doi=10.1038%2fs41598-018-33911-z&partnerID=40&md5=1064459d23dca3d831e79ec895b3b92d},

doi = {10.1038/s41598-018-33911-z},

year  = {2018},

date = {2018-01-01},

journal = {Scientific Reports},

volume = {8},

number = {1},

note = {Q1, 15/69 MS, 4.011 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carballal20181,

title = {Automatic multiscale vascular image segmentation algorithm for coronary angiography},

author = {A. Carballal and F. J. Novoa and C. Fernandez-Lozano and M. García-Guimaraes and G. Aldama-López and R. Calviño-Santos and J. M. Vazquez-Rodriguez and A. Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049482367&doi=10.1016%2fj.bspc.2018.06.007&partnerID=40&md5=a9ba9b7cee145c788ee09e4bf743d560},

doi = {10.1016/j.bspc.2018.06.007},

year  = {2018},

date = {2018-01-01},

journal = {Biomedical Signal Processing and Control},

volume = {46},

pages = {1-9},

note = {Q2, 27/80 EBIO, 2.943 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2017306,

title = {Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity},

author = {Y. Liu and T. Shaoxun and C. Fernandez-Lozano and C. R. Munteanu and A. Pazos and Y. Yu and Z. Tan and H. González-Díaz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006699870&doi=10.1016%2fj.eswa.2016.10.058&partnerID=40&md5=f5c9886eba16d6fb7fc5e79ac5646e2f},

doi = {10.1016/j.eswa.2016.10.058},

year  = {2017},

date = {2017-01-01},

journal = {Expert Systems with Applications},

volume = {72},

pages = {306-316},

note = {Q1, 19/130 CS-AI, 2.981 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pérez-Caballero2017117,

title = {Authentication of tequilas using pattern recognition and supervised classification},

author = {G. Pérez-Caballero and J. M. Andrade and P. Olmos and Y. Molina and I. Jiménez and J. J. Durán and C. Fernandez-Lozano and F. Miguel-Cruz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026452240&doi=10.1016%2fj.trac.2017.07.008&partnerID=40&md5=43738f353da8ec2b9d62926c101e5aa3},

doi = {10.1016/j.trac.2017.07.008},

year  = {2017},

date = {2017-01-01},

journal = {TrAC - Trends in Analytical Chemistry},

volume = {94},

pages = {117-129},

note = {Q1, <strong>D1</strong>, 3/81, CA, 7.030 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alonso-Calvo2017179,

title = {A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer},

author = {R. Alonso-Calvo and S. Paraiso-Medina and D. Perez-Rey and E. Alonso-Oset and R. Stiphout and S. Yu and M. Taylor and F. Buffa and C. Fernandez-Lozano and A. Pazos and V. Maojo},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020462913&doi=10.1016%2fj.compbiomed.2017.06.005&partnerID=40&md5=38d5e5ad7165cddcf1c049b6b1c64018},

doi = {10.1016/j.compbiomed.2017.06.005},

year  = {2017},

date = {2017-01-01},

journal = {Computers in Biology and Medicine},

volume = {87},

pages = {179-186},

note = {Q3, 53/104 CS-IA, 2.755 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2017,

title = {Experimental study and ANN dual-time scale perturbation model of electrokinetic properties of microbiota},

author = {Y. Liu and C. R. Munteanu and C. Fernandez-Lozano and A. Pazos and T. Ran and Z. Tan and Y. Yu and C. Zhou and S. Tang and H. González-Díaz},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021707468&doi=10.3389%2ffmicb.2017.01216&partnerID=40&md5=90d8f2cc3d64b7a15584abc695e185eb},

doi = {10.3389/fmicb.2017.01216},

year  = {2017},

date = {2017-01-01},

journal = {Frontiers in Microbiology},

volume = {8},

number = {JUN},

note = {Q2, 32/126 MIC, 4.019 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano2016,

title = {Texture analysis in gel electrophoresis images using an integrative kernel-based approach},

author = {C. Fernandez-Lozano and J. A. Seoane and M. Gestal and T. R. Gaunt and J. Dorado and A. Pazos and C. Campbell},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954542295&doi=10.1038%2fsrep19256&partnerID=40&md5=5f8b9735a9992b6c34bfff7410b91a0e},

doi = {10.1038/srep19256},

year  = {2016},

date = {2016-01-01},

journal = {Scientific Reports},

volume = {6},

note = {Q1, 7/63 MS, 5.228 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano2016c,

title = {A methodology for the design of experiments in computational intelligence with multiple regression models},

author = {C. Fernandez-Lozano and M. Gestal and C. R. Munteanu and J. Dorado and A. Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85003816265&doi=10.7717%2fpeerj.2721&partnerID=40&md5=960cae21e6f5ff3cbedbdb4de62bbdac},

doi = {10.7717/peerj.2721},

year  = {2016},

date = {2016-01-01},

journal = {PeerJ},

volume = {2016},

number = {12},

note = {Q1, 14/63 MS, 2.183 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano2016995,

title = {Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils},

author = {C. Fernandez-Lozano and F. Cedrón and D. Rivero and J. Dorado and J. Manuel Andrade-Garda and A. Pazos and M. Gestal},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971254378&doi=10.1108%2fEC-03-2015-0062&partnerID=40&md5=ed4857f9a0020face7149b56b1b75986},

doi = {10.1108/EC-03-2015-0062},

year  = {2016},

date = {2016-01-01},

journal = {Engineering Computations (Swansea, Wales)},

volume = {33},

number = {4},

pages = {995-1005},

note = {Q2, 42/102 CS-IA, 1.495 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Munteanu20156205,

title = {Classification of mild cognitive impairment and Alzheimer's Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data},

author = {C. R. Munteanu and C. Fernandez-Lozano and V. Mato Abad and S. Pita Fernández and J. Álvarez-Linera and J. A. Hernández-Tamames and A. Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937763002&doi=10.1016%2fj.eswa.2015.03.011&partnerID=40&md5=54d45f0606b3710f76c8283003fe1203},

doi = {10.1016/j.eswa.2015.03.011},

year  = {2015},

date = {2015-01-01},

journal = {Expert Systems with Applications},

volume = {42},

number = {15-16},

pages = {6205-6214},

note = {Q1, 30/121 CS-AI, 1.965 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Munteanu20151077,

title = {Solvent accessible surface area-based hot-spot detection methods for protein-protein and protein-nucleic acid interfaces},

author = {C. R. Munteanu and A. C. Pimenta and C. Fernandez-Lozano and A. Melo and M. N. D. S. Cordeiro and I. S. Moreira},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930213013&doi=10.1021%2fci500760m&partnerID=40&md5=574f5787dafad6f337eec21869439fc5},

doi = {10.1021/ci500760m},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Chemical Information and Modeling},

volume = {55},

number = {5},

pages = {1077-1086},

note = {Q1, <strong>D1</strong>, 6/135 CS-IS, 4.068 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Blanco2015672,

title = {A hybrid evolutionary system for automated artificial neural networks generation and simplification in biomedical applications},

author = {E. Fernandez-Blanco and D. Rivero and M. Gestal and C. Fernández-Lozano and N. Ezquerra and C. R. Munteanu and J. Dorado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953872044&doi=10.2174%2f1574893610666151008012923&partnerID=40&md5=f75594749d1c0d435e263203bfd11f94},

doi = {10.2174/1574893610666151008012923},

year  = {2015},

date = {2015-01-01},

journal = {Current Bioinformatics},

volume = {10},

pages = {672-691},

note = {Q2, 20/52 M-CB, 1.726 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Tsiliki2015,

title = {RRegrs: An R package for computer-aided model selection with multiple regression models},

author = {G. Tsiliki and C. R. Munteanu and J. A. Seoane and C. Fernandez-Lozano and H. Sarimveis and E. L. Willighagen},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941650396&doi=10.1186%2fs13321-015-0094-2&partnerID=40&md5=e537fd382211e643e14176759e42d119},

doi = {10.1186/s13321-015-0094-2},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Cheminformatics},

volume = {7},

number = {1},

note = {Q1, <strong>D1</strong>, 4/139 CS-IS, 4.547 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano20152469,

title = {Texture classification using feature selection and kernel-based techniques},

author = {C. Fernandez-Lozano and J. A. Seoane and M. Gestal and T. R. Gaunt and J. Dorado and C. Campbell},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939267748&doi=10.1007%2fs00500-014-1573-5&partnerID=40&md5=0a303adb5457586dfbb0ea5e772f6f76},

doi = {10.1007/s00500-014-1573-5},

year  = {2015},

date = {2015-01-01},

journal = {Soft Computing},

volume = {19},

number = {9},

pages = {2469-2480},

note = {Q2, 59/121 CS-AI, 1.304 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano201550,

title = {Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models},

author = {C. Fernandez-Lozano and R. F. Cuiñas and J. A. Seoane and E. Fernández-Blanco and J. Dorado and C. R. Munteanu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84940395729&doi=10.1016%2fj.jtbi.2015.07.038&partnerID=40&md5=6b80853f06b73681a99349b834fe3989},

doi = {10.1016/j.jtbi.2015.07.038},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Theoretical Biology},

volume = {384},

pages = {50-58},

note = {Q2, 15/56 M-CB, 2.116 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Rodriguez201453,

title = {Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models},

author = {A. Rodriguez and C. Fernandez-Lozano and J. Dorado and J. R. Rabuñal},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898687524&doi=10.1016%2fj.ab.2014.02.027&partnerID=40&md5=ac129bd9762fa319e777f2be5fd90338},

doi = {10.1016/j.ab.2014.02.027},

year  = {2014},

date = {2014-01-01},

journal = {Analytical Biochemistry},

volume = {454},

number = {1},

pages = {53-59},

note = {Q2, 32/75 C-A, 3.325 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano201412,

title = {Markov mean properties for cell death-related protein classification},

author = {C. Fernandez-Lozano and M. Gestal and H. González-Díaz and J. Dorado and A. Pazos and C. R. Munteanu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894089315&doi=10.1016%2fj.jtbi.2014.01.033&partnerID=40&md5=16617077f207ad011f4e158984873161},

doi = {10.1016/j.jtbi.2014.01.033},

year  = {2014},

date = {2014-01-01},

journal = {Journal of Theoretical Biology},

volume = {349},

pages = {12-21},

note = {Q2, 23/85 M-CB, 2.116 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano20141063,

title = {Improving enzyme regulatory protein classification by means of SVM-RFE feature selection},

author = {C. Fernandez-Lozano and E. Fernández-Blanco and K. Dave and N. Pedreira and M. Gestal and J. Dorado and C. R. Munteanu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897561080&doi=10.1039%2fc3mb70489k&partnerID=40&md5=9fa9fb706f730b7d448f6a68813caed6},

doi = {10.1039/c3mb70489k},

year  = {2014},

date = {2014-01-01},

journal = {Molecular BioSystems},

volume = {10},

number = {5},

pages = {1063-1071},

note = {Q2, 110/289 B-MB, 3.210 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano2013309,

title = {High order texture-based analysis in biomedical images},

author = {C. Fernandez-Lozano and M. Gestal and N. Pedreira and J. Dorado and A. Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893472168&doi=10.2174%2f15734056113096660005&partnerID=40&md5=2b43c6dac51364d4a30860118da38ae2},

doi = {10.2174/15734056113096660005},

year  = {2013},

date = {2013-01-01},

journal = {Current Medical Imaging Reviews},

volume = {9},

number = {4},

pages = {309-317},

note = {Q4, 94/121 RNM-MI, 1.059 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano2013,

title = {Hybrid model based on genetic algorithms and SVM applied to variable selection within fruit juice classification},

author = {C. Fernandez-Lozano and C. Canto and M. Gestal and J. M. Andrade-Garda and J. R. Rabuñal and J. Dorado and A. Pazos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893829022&doi=10.1155%2f2013%2f982438&partnerID=40&md5=5a27105a564c7c71d5ac01dd8f2ff22f},

doi = {10.1155/2013/982438},

year  = {2013},

date = {2013-01-01},

journal = {The Scientific World Journal},

volume = {2013},

note = {Q2, 16/55 CS-AI, 1.219 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aguiar-Pulido2013675,

title = {Applied computational techniques on schizophrenia using genetic mutations},

author = {V. Aguiar-Pulido and M. Gestal and C. Fernandez-Lozano and D. Rivero and C. R. Munteanu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878546734&doi=10.2174%2f1568026611313050010&partnerID=40&md5=37ba427c1d94538ac1e642524bf65ab8},

doi = {10.2174/1568026611313050010},

year  = {2013},

date = {2013-01-01},

journal = {Current Topics in Medicinal Chemistry},

volume = {13},

number = {5},

pages = {675-684},

note = {Q1, 11/58 CA, 3.453 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fernandez-Lozano20131681,

title = {Kernel-based feature selection techniques for transport proteins based on Star graph topological indices},

author = {C. Fernandez-Lozano and M. Gestal and N. Pedreira-Souto and L. Postelnicu and J. Dorado and C. R. Munteanu},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887866644&doi=10.2174%2f15680266113139990119&partnerID=40&md5=e9449ca3da3dba43ce72866e9f0d98be},

doi = {10.2174/15680266113139990119},

year  = {2013},

date = {2013-01-01},

journal = {Current Topics in Medicinal Chemistry},

volume = {13},

number = {14},

pages = {1681-1691},

note = {Q1, 11/58 CA, 3.453 IF},

keywords = {},

pubstate = {published},

tppubtype = {article}

}