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Gerardo Casañola-Martin   Dr.  Post Doctoral Researcher 
Department of Coatings and Polymeric Materials, North Dakota State University , Fargo, North Dakota, United States
Central University of Las Villas
Bachelor, Chemistry
1998 - 2003 | Chemistry
Central University of Las Villas
Ph.D., Chemistry
2005 - 2009 | Chemistry
Universitat de Valencia
Other, Chemoinformatics and Enzyme Inhibitors
2010 - 2010 | Postdoctoral Fellowship
Universitat de Valencia
Other, Chemoinformatics
2013 - 2014 | Postdoctoral Fellowship
Carleton University
Other, Postranslation modification of UPP
2016 - 2017 | Postdoctoral Fellowship
North Dakota State University
Other, Computational Design of Bio-Based Polymers
2018 - 2019 | Postdoctoral Fellowship
Timeline See timeline
Gerardo Casañola-Martin published an article in January 2018.
Research Keywords & Expertise See all
0 Big Data Analytics
0 Bioinformatics
0 Data Mining
0 Machine Learning
0 Polymer Chemistry
0 Python Script
Top co-authors See all
Humberto González-Díaz

215 shared publications

Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain

José M. Monserrat

185 shared publications

Programa de Pós-Graduação em Aquicultura; Universidade Federal do Rio Grande-FURG; Rio Grande Brazil

Yovani Marrero-Ponce

158 shared publications

Universidad San Francisco de Quito (USFQ); Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas; Quito, Pichincha Ecuador

Cristian-Robert Munteanu

112 shared publications

Department of Computation, Faculty of Computer Science, A Coruña, Spain

Bakhtiyor Rasulev

89 shared publications

Department of Coatings and Polymeric MaterialsNorth Dakota State University Fargo, ND 58105 USA

Publication Record
Distribution of Articles published per year 
(2006 - 2018)
Total number of journals
published in
Publications See all
Article 1 Read 0 Citations Atom based linear index descriptors in QSAR-machine learning classifiers for the prediction of ubiquitin-proteasome path... Gerardo M. Casañola-Martin, Hai Pham-The, Juan A. Castillo-G... Published: 17 January 2018
Medicinal Chemistry Research, doi: 10.1007/s00044-017-2091-7
DOI See at publisher website
Article 7 Reads 0 Citations Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Mod... Michael González-Durruthy, Jose M. Monserrat, Bakhtiyor Rasu... Published: 11 November 2017
Nanomaterials, doi: 10.3390/nano7110386
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of Jm showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of Jm for other CNTs was provided by random forest using eight features, obtaining test R-squared (R2) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.
CONFERENCE-ARTICLE 11 Reads 0 Citations <strong>Blood-Brain Barrier Passage Prediction Using Decision Tree</strong> Juan Castillo-Garit, Gerardo Casañola-Martín, Huong Le-Thi- ... Published: 16 October 2017
Proceedings of MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition, doi: 10.3390/mol2net-03-04627
DOI See at publisher website ABS Show/hide abstract

In this report, the blood brain barrier (BBB) permeability prediction is carried out using a decision tree. A recently published data set of 497 compounds is selected to develop the tree model. The developed model shows an accuracy of 87.66% for training set; 86.09% in the 10-fold cross-validation procedure and 87.93% for the test set. Some structural explanation of how our model describe the passage of molecules through the BBB is given. Moreover, a comparison with other approaches is carried out showing good behaviour of our method. Finally, we can say that, the present results could represent a useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.

CONFERENCE-ARTICLE 18 Reads 0 Citations <strong>Perturbation Theory Model of Metabolic Reaction Networks </strong> Gerardo M. Casañola-Martín, Facundo Pérez-Jiménez, Matilde M... Published: 26 September 2017
Proceedings of MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition, doi: 10.3390/mol2net-03-04612
DOI See at publisher website ABS Show/hide abstract

In this work, we used Perturbation Theory (PT) techniques to define a linear model for metabolic pathway networks of >40 organisms compiled by Barabasis’ group. We calculated PT operators for 150000 pairs of nodes (metabolites) using Markov linear indices fk. The linear CPTML model obtained predicts network topology with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series.

Article 5 Reads 0 Citations Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis J. A. Castillo-Garit, G. M. Casañola-Martin, S. J. Barigye, ... Published: 02 September 2017
SAR and QSAR in Environmental Research, doi: 10.1080/1062936X.2017.1376705
DOI See at publisher website
Article 0 Reads 1 Citation Quantitative structure-activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors fro... Hai Pham-The, Gerardo Casañola-Martin, Karel Diéguez Santana... Published: 28 February 2017
SAR and QSAR in Environmental Research, doi: 10.1080/1062936X.2017.1294198
DOI See at publisher website PubMed View at PubMed