Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales

ilustraciones, graficas

Autores:: Valencia Colman, Laura Sofía

Tipo de recurso:

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
dc.title.translated.eng.fl_str_mv	Classification of semiochemicals associated with coleoptera of the Polyphaga suborder by means of artificial neural networks
title	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
spellingShingle	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales 540 - Química y ciencias afines Descriptores Moleculares Análisis de Componentes Principales Bosques Aleatorios Boruta-SHAP C-means Mapas Autoorganizados de Kohonen Perceptrón Multicapa Molecular Descriptors Principal Component Analysis Random Forests Boruta-SHAP C-means Kohonen Self Organizing Maps Multilayer Perceptron
title_short	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
title_full	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
title_fullStr	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
title_full_unstemmed	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
title_sort	Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales
dc.creator.fl_str_mv	Valencia Colman, Laura Sofía
dc.contributor.advisor.none.fl_str_mv	Daza Caicedo, Edgar Eduardo
dc.contributor.author.none.fl_str_mv	Valencia Colman, Laura Sofía
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Química Teórica
dc.subject.ddc.spa.fl_str_mv	540 - Química y ciencias afines
topic	540 - Química y ciencias afines Descriptores Moleculares Análisis de Componentes Principales Bosques Aleatorios Boruta-SHAP C-means Mapas Autoorganizados de Kohonen Perceptrón Multicapa Molecular Descriptors Principal Component Analysis Random Forests Boruta-SHAP C-means Kohonen Self Organizing Maps Multilayer Perceptron
dc.subject.proposal.spa.fl_str_mv	Descriptores Moleculares Análisis de Componentes Principales Bosques Aleatorios Boruta-SHAP C-means Mapas Autoorganizados de Kohonen Perceptrón Multicapa
dc.subject.proposal.eng.fl_str_mv	Molecular Descriptors Principal Component Analysis Random Forests Boruta-SHAP C-means Kohonen Self Organizing Maps Multilayer Perceptron
description	ilustraciones, graficas
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-08-29T22:52:16Z
dc.date.available.none.fl_str_mv	2022-08-29T22:52:16Z
dc.date.issued.none.fl_str_mv	2022
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/82182
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/82182 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	Abdi, H. & Williams, L. J. Principal component analysis Wiley interdisciplinary reviews: computational statistics, Wiley Online Library, 2010, 2, 433-459 Allison, J. D.; Borden, J. H. & Seybold, S. J. A review of the chemical ecology of the Cerambycidae (Coleoptera) Chemoecology, Springer, 2004, 14, 123-150 Allouche, A.-R. Gabedit—A graphical user interface for computational chemistry softwares Journal of computational chemistry, Wiley Online Library, 2011, 32, 174-182 Assembly, G. The International Committee on Bionomenclature, 2011 Bakthavatsalam, N. Semiochemicals Ecofriendly pest management for food security, Elsevier, 2016, 563-611 Bezdek, J. C.; Ehrlich, R. & Full, W. FCM: The fuzzy c-means clustering algorithm Computers & geosciences, Elsevier, 1984, 10, 191-203 Black, P. E. & others Algorithms and Theory of Computation Handbook Dictionary of algorithms and data structures, CRC Press LLC, 1999 Blomquist, G. & Vogt, R. (Eds.) Insect Pheromone Biochemistry and Molecular Biology, Elsevier Academic Press, 2003 Brownlee, J. Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python, Machine Learning Mastery, 2020 Burns, J. A. & Whitesides, G. M. Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition, Chemical Reviews, ACS Publications, 1993, 93, 2583-2601 Cai, D.; Zhang, C. & He, X. Unsupervised feature selection for multi-cluster data, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, 333-342 Cebeci, Z.; Yildiz, F.; Kavlak, A.; Cebeci, C. & Onder, H. ppclust: Probabilistic and Possibilistic Cluster Analysis, R package version 0.1, 2019, 3 Chen, R.-C.; Dewi, C.; Huang, S.-W. & Caraka, R. E. Selecting critical features for data classification based on machine learning methods, Journal of Big Data, Springer, 2020, 7, 1-26 Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns, Front. Chem., 2019, 7, 809. David, L.; Thakkar, A.; Mercado, R. & Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide, Journal of Cheminformatics, BioMed Central, 2020, 12, 1-22 Delgadillo, D. Feromonas. Lo que el viento se llevó, Revista Casa del Tiempo, 2005, 3 Dong, J.; Cao, D.-S.; Miao, H.-Y.; Liu, S.; Deng, B.-C.; Yun, Y.-H.; Wang, N.-N.; Lu, A.-P.; Zeng, W.-B. & Chen, A. F. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation, Journal of cheminformatics, BioMed Central, 2015, 7, 60 Effrosynidis, D. & Arampatzis, A. An evaluation of feature selection methods for environmental data, Ecological Informatics, Elsevier, 2021, 61, 101224 El-Sayed, A. M. The pherobase: database of insect pheromones and semiochemicals, 2022 Ezzat, S. M.; Jeevanandam, J.; Egbuna, C.; Merghany, R. M.; Akram, M.; Daniyal, M.; Nisar, J. & Sharif, A. Semiochemicals: A Green Approach to Pest and Disease Control, Natural Remedies for Pest, Disease and Weed Control, Elsevier, 2020, 81-89 Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery Jr., J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B. & Fox, D. J. Gaussian16 Revision C.01 2016 Genuer, R.; Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests, Pattern recognition letters, Elsevier, 2010, 31, 2225-2236 Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, 2019 Ghosh, S. & Dubey, S. K. Comparative analysis of k-means and fuzzy c-means algorithms International, Journal of Advanced Computer Science and Applications, Citeseer, 2013, 4 Gitau, C.; Bashford, R.; Carnegie, A. & Gurr, G. A review of semiochemicals associated with bark beetle (Coleoptera: Curculionidae: Scolytinae) pests of coniferous trees: a focus on beetle interactions with other pests and their associates, Forest Ecology and Management, Elsevier, 2013, 297, 1-14 Grinberg, M. Flask web development: developing web applications with python, O'Reilly Media, Inc., 2018 Janet, J. P. & Kulik, H. J. Machine Learning in Chemistry, American Chemical Society, 2020 Jaramillo-Noreña, J.; León, G. D. S.; Rodríguez, V. P.; Garzón, D. Q.; Cuartas, M. Á. Z. & Arroyave, M. G. Capitulo 7: Manejo integrado de plagas, Tecnología para el cultivo de tomate bajo condiciones protegidas, Corporación Colombiana de Investigación Agropecuaria, 2013 Judd, W. S.; Campbell, C. S.; Kellogg, E. A.; Stevens, P. F. & Donoghue, M. J. Plant systematics: a phylogenetic approach, Ecología mediterranea, 1999, 25, 215 Kalousis, A.; Prados, J. & Hilario, M. Stability of feature selection algorithms: a study on high-dimensional spaces, Knowledge and information systems, Springer, 2007, 12, 95-116 Kasabov, N. K. Time-space, spiking neural networks and brain-inspired artificial intelligence, Springer, 2019 Kassambara, A. Multivariate Analysis II: Practical Guide to Principal Component Methods in R, Scotts Valley, CA: CreateSpace Independent Publishing Platform.[Google Scholar], 2017 Kassambara, A. Practical guide to principal component methods in R: PCA, M (CA), FAMD, MFA, HCPC, factoextra, Sthda, 2017, 2 Keany, E. BorutaShap, MIT License, 2021 Kohonen, T. Self-organized formation of topologically correct feature maps, Biological cybernetics, Springer, 1982, 43, 59-69 Kohonen, T. The self-organizing map, Proceedings of the IEEE, IEEE, 1990, 78, 1464-1480 Kramer, O. Scikit-learn, Machine learning for evolution strategies, Springer, 2016, 45-53 Kuhn, M. & Johnson, K. Feature engineering and selection: A practical approach for predictive models, CRC Press, 2019 Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package, Journal of statistical software, 2010, 36, 1-13 Kuzma, T. & Farkaš, I. Embedding Complexity of Learned Representations in Neural Networks, International Conference on Artificial Neural Networks, 2019, 518-528 Lal, T. N.; Chapelle, O.; Weston, J. & Elisseeff, A. Embedded methods, Feature extraction, Springer, 2006, 137-165 Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions, Advances in neural information processing systems, 2017, 30 Maltarollo, V. G.; Honório, K. M. & da Silva, A. B. F. Suzuki, K. (Ed.) Applications of Artificial Neural Networks in Chemical Problems, Chap.10, Artificial Neural Networks, IntechOpen, 2013 Mauri, A.; Consonni, V. & Todeschini, R. Molecular descriptors, Springer International Publishing, 2017 McHugh, M. L. Interrater reliability: the kappa statistic, Biochemia medica, Medicinska naklada, 2012, 22, 276-282 Mishra, S. S.; Shroff, S.; Sahu, J. K.; Naik, P. P. & Baitharu, I. Insect Pheromones and Its Applications in Management of Pest Population, Natural Materials and Products from Insects: Chemistry and Applications, Springer, 2020, 121-136 Mitchell, T. M. & others Machine learning, McGraw-hill New York, 1997 Moretto Cosson, K. y. A. Pollination of Amorphophallus barthlottii and A. abyssinicus subsp. akeassii (Araceae) by dung beetles (Insecta: Coleoptera: Scarabaeoidea), Association Catharsius, 2019 Morgan, E. D. Biosynthesis in insects, Royal society of chemistry, 2010 Moriwaki, H.; Tian, Y.-S.; Kawashita, N. & Takagi, T. Mordred: a molecular descriptor calculator, Journal of cheminformatics, BioMed Central, 2018, 10, 1-14 Mustaqeem, A.; Anwar, S. M. & Majid, M. Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants, Computational and mathematical methods in medicine, Hindawi, 2018, 2018 MySQL, A. MySQL, 2001 Odell, S. G.; Lazo, G. R.; Woodhouse, M. R.; Hane, D. L. & Sen, T. Z. The art of curation at a biological database: principles and application, Current Plant Biology, Elsevier, 2017, 11, 2-11 Pardo-Locarno, L.; González, J.; Pérez, C.; Yepes, F. & Fernández, C. Escarabajos de importancia agrícola (Coleoptera: Melolonthidae) en la región Caribe colombiana: Registros y propuestas de manejo, Boletín Del Museo Entomológico Francisco Luis Gallejo, 2012, 4, 7-23 Peterson, M. A.; Dobler, S.; Larson, E. L.; Juárez, D.; Schlarbaum, T.; Monsen, K. J. & Francke, W. Profiles of cuticular hydrocarbons mediate male mate choice and sexual isolation between hybridising Chrysochus (Coleoptera: Chrysomelidae), Chemoecology, Springer, 2007, 17, 87-96 Piñeiro Gomez, J. Diseño de bases de datos relacionales, Ediciones Paraninfo, SA, 2014 Pla, L. Análisis multivariado: método de componentes principales, OEA (Organizacion de los Estados Americanos), 1986 Raices, M. Comunicación química en larvas de Rhinella arenarum. Caracterización del comportamiento antipredatorio y de las señales de alarma, Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, 2018 Raju, M.; Gopi, V. P. & Anitha, V. Multi-class Classification of Alzheimer's Disease using 3DCNN Features and Multilayer Perceptron, 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2021, 368-373 Romero-Frías, A.; Murata, Y.; Simões Bento, J. M. & Osorio, C. (1R, 2S, 6R)-Papayanal: a new male-specific volatile compound released by the guava weevil Conotrachelus psidii (Coleoptera: Curculionidae), Bioscience, Biotechnology, and Biochemistry, Oxford University Press, 2016, 80, 848-855 Romero-Frías, A. A.; Sinuco, D. C. & Bento, J. M. S. Male-specific volatiles released by the big avocado seed weevil Heilipus lauri Boheman (Coleoptera: Curculionidae), Journal of the Brazilian Chemical Society, SciELO Brasil, 2019, 30, 158-163 Romero-López, A. A.; Reyes-Chilpa, R.; Pérez-Flores, F. J.; Lugo-García, G. A. & Maldonado-Rodríguez, J. I. Chemicals in the Genital Chamber of Two Mexican Species of Phyllophaga, Southwestern Entomologist, Society of Southwestern Entomologists, 2019, 44, 457 - 464 Samuel, A. L. Some studies in machine learning using the game of checkers, IBM Journal of research and development, IBM, 1959, 3, 210-229 Sandri, M. & Zuccolotto, P. Variable selection using random forests, Data analysis, classification and the forward search, Springer, 2006, 263-270 Sharma, A.; Sandhi, R. K. & Reddy, G. V. A Review of Interactions between Insect Biological Control Agents and Semiochemicals, Insects, Multidisciplinary Digital Publishing Institute, 2019, 10, 439 Shibata, E.; Sato, S.; Sakuratani, Y.; Sugimoto, T.; Kimura, F. & Ito, F. Cerambycid beetles (Coleoptera) lured to chemicals in forests of Nara Prefecture, central Japan, Annals of the Entomological Society of America, Oxford University Press Oxford, UK, 1996, 89, 835-842 Silva, W. D.; Millar, J. G.; Hanks, L. M.; Costa, C. M.; Leite, M. O.; Tonelli, M. & Bento, J. M. S. Interspecific cross-attraction between the South American cerambycid beetles Cotyclytus curvatus and Megacyllene acuta is averted by minor pheromone components, Journal of chemical ecology, Springer, 2018, 44, 268-275 Smart, L.; Aradottir, G. & Bruce, T. Role of semiochemicals in integrated pest management, Integrated Pest Management, Elsevier, 2014, 93-109 Sneath, P. H. Thirty years of numerical taxonomy, Systematic Biology, Society of Systematic Biologists, 1995, 44, 281-298 Solorio-Fernández, S.; Carrasco-Ochoa, J. A. & Mart\inez-Trinidad, J. F. A review of unsupervised feature selection methods, Artificial Intelligence Review, Springer, 2020, 53, 907-948 Spurlock, J. Bootstrap: responsive web development, O'Reilly Media, Inc., 2013 Stanczyk, S.; Champion, B. & Leyton, R. Theory and practice of relational databases, CRC Press, 2003 Syakur, M.; Khotimah, B.; Rochman, E. & Satoto, B. D. Integration k-means clustering method and elbow method for identification of the best customer profile cluster, IOP conference series: materials science and engineering, 2018, 336, 012017 Tauler, R.; Walczak, B. & Brown, S. D. Comprehensive chemometrics: chemical and biochemical data analysis, Elsevier, 2009 Thai, V. D.; Hoan, N. Q. & others Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function, International Journal of Computer Science and Information Security, LJS Publishing, 2016, 14, 746 Todeschini, R. & Consonni, V. Handbook of molecular descriptors, John Wiley & Sons, 2008 Touzet, H. Tree edit distance with gaps, Information Processing Letters, Citeseer, 2003, 85, 123-129 Uriarte, E. A. & Mart\in, F. D. Topology preservation in SOM, International journal of applied mathematics and computer sciences, Citeseer, 2005, 1, 19-22 Vettigli, G. MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map, 2013 Vidal Medina, V. Señales químicas entre el escarabajo-plaga Strategus aloeus (Coleoptera: Scarabaeidae: Dynastinae) y la palma de aceite (Elaeis guineensis Jacq.), Universidad Nacional de Colombia, Universidad Nacional de Colombia, 2021, 120 Warner, J. & Sexauer, J. JDWarner/scikit-fuzzy: Scikit-Fuzzy version 0.4. 2 Nov, 2019 Wei, J. N.; Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions, ACS central science, ACS Publications, 2016, 2, 725-732
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Química
dc.publisher.department.spa.fl_str_mv	Departamento de Química
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Daza Caicedo, Edgar Eduardod478d46b83a39179002cacb1622589ddValencia Colman, Laura Sofíac43a56fd61ad1425dcf264d2cb283147Grupo de Química Teórica2022-08-29T22:52:16Z2022-08-29T22:52:16Z2022https://repositorio.unal.edu.co/handle/unal/82182Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasEn esta investigación buscamos establecer la relación entre los compuesto que median la interacción y el mensaje que transmiten para los coleópteros del suborden Polyphaga. Para ello, nos propusimos desarrollar herramientas de aprendizaje de máquina para predecir la respuesta de un individuo al exponerse a un cierto compuesto; es decir, establecer la naturaleza del semioquímico según la especie a la que pertenezca el individuo y, a la vez, buscar patrones entre estas moléculas. Construimos una base de datos relacional basada en el lenguaje SQL en la que almacenamos información correspondiente a las categorías taxonómicas de los insectos, sus hospederos y los semioquímicos reportados para cada especie; así como, el tipo de semioquímico, es decir, si es feromona (de agregación, de rastro, sexual, ovoposición, etc) o aleloquímico (cairomona, sinomona o alomona); si presenta atracción específica (macho y/o hembra) y la metodología mediante la cual se evaluó su actividad (pruebas de campo, electroantenografía u olfatometría). La información con la cual alimentamos esta base de datos provino de una revisión de 957 artículos publicados en revistas especializadas, en los cuales se reportan 981 compuestos como semioquímicos. Para implementar las técnicas de aprendizaje de máquina, requerimos una caracterización cuantitativa tanto de la estructura química de cada uno de los semioquímicos, como de la clasificación taxonómica de los insectos. Para lo primero empleamos un conjunto de 1287 descriptores moleculares, este conjunto es hiper-redundante dado que se busca poder incluir la mayor cantidad de información sobre las características de cada compuesto y su posible vínculo con la propiedad esperada. Para la caracterización de las categorías taxonómicas creamos un código taxonómico numérico capaz de dar cuenta de la similitud de dos especies. Una vez calculamos las variables procedimos a seleccionar los más discriminantes o apropiados para una clasificación dada. El proceso de selección de variables lo hicimos con las técnicas de Análisis de Componentes Principales, Bosques Aleatorios y Boruta-SHAP. Para la predicción de la función de los semioquímicos y la búsqueda de patrones entre ellos implementamos los algoritmos de: C-means, mapas auto-organizados de Kohonen y perceptrones multicapa; todos empleando Python. La combinación de estas herramientas nos permitió dilucidar un primer patrón de clasificación relacionado con su origen biosintético y así clasificar el conjunto de semioquímicos según de las rutas biosintéticas de las cuales se derivan. Además, logramos establecer un modelo capaz de asignar el tipo de mensaje que transmite un compuesto dado, es decir la función que cumple para la pareja insecto-molécula; en otras palabras adscribimos una función a cada semioquímico dependiendo del insecto con que interactúa. (Texto tomado de la fuente)In this research we seek to establish the relationship between the compounds that mediate the interaction and the message they transmit for beetles of the suborder Polyphaga. Consequently, we set out to develop tools employing machine learning to predict the response of each individual when exposed to a certain compound; that is, to establish the nature of the semiochemical according to the species to which the individual belongs, and at the same time look for patterns between these molecules. We built a relational database based on SQL language in which we store information corresponding to the taxonomic categories of insects, their hosts and the semiochemicals reported for each species; as well as the type of semiochemical, that is, if it corresponds to a pheromone (aggregation, trace, sexual, oviposition, etc.) or an allelochemical (kairomone, sinomone or allomone); if it presents specific attraction (male, female, larva) and the methodology by which its activity was evaluated (field tests, electroantenography or olfactometry). The information with which we fed this database came from a review of 957 articles published in specialized journals, in which 981 compounds are reported as semiochemicals. To implement machine learning techniques, we require a quantitative characterization of both the chemical structure of each of the semiochemicals and the taxonomic classification of insects. For the first, we use a set of 1287 molecular descriptors, this set is hyper-redundant since it seeks to be able to include the greatest amount of information about the characteristics of each compound and its possible linkage with the expected property. For the characterization of the taxonomic categories we created a numerical taxonomic code capable of accounting for the similarity of two species. Once we calculated the variables, we proceeded to select the most discriminating or appropriate for a given classification. The variable selection process was carried out using Principal Component Analysis, Random Forest and Boruta-SHAP techniques. For the prediction of the function of semiochemicals and the search for patterns between them, we implement the following algorithms: C-means, Kohonen self-organized maps and multilayer perceptrons; all using Python. The combination of these tools allowed us to elucidate a first classification pattern related to their biosynthetic origin and thus classify the set of semiochemicals according to the biosynthetic routes from which they are derived. In addition, we managed to establish a model capable of assigning the type of message transmitted by a given compound, that is, the function it fulfills for the insect-molecule pair; in other words, we ascribe a function to each semiochemical depending on the insect with which it interacts.MaestríaMagíster en Ciencias - QuímicaQuímica computacionalEcología químicaxii, 55 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - QuímicaDepartamento de QuímicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá540 - Química y ciencias afinesDescriptores MolecularesAnálisis de Componentes PrincipalesBosques AleatoriosBoruta-SHAPC-meansMapas Autoorganizados de KohonenPerceptrón MulticapaMolecular DescriptorsPrincipal Component AnalysisRandom ForestsBoruta-SHAPC-meansKohonen Self Organizing MapsMultilayer PerceptronClasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificialesClassification of semiochemicals associated with coleoptera of the Polyphaga suborder by means of artificial neural networksTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaAbdi, H. & Williams, L. J. Principal component analysis Wiley interdisciplinary reviews: computational statistics, Wiley Online Library, 2010, 2, 433-459Allison, J. D.; Borden, J. H. & Seybold, S. J. A review of the chemical ecology of the Cerambycidae (Coleoptera) Chemoecology, Springer, 2004, 14, 123-150Allouche, A.-R. Gabedit—A graphical user interface for computational chemistry softwares Journal of computational chemistry, Wiley Online Library, 2011, 32, 174-182Assembly, G. The International Committee on Bionomenclature, 2011Bakthavatsalam, N. Semiochemicals Ecofriendly pest management for food security, Elsevier, 2016, 563-611Bezdek, J. C.; Ehrlich, R. & Full, W. FCM: The fuzzy c-means clustering algorithm Computers & geosciences, Elsevier, 1984, 10, 191-203Black, P. E. & others Algorithms and Theory of Computation Handbook Dictionary of algorithms and data structures, CRC Press LLC, 1999Blomquist, G. & Vogt, R. (Eds.) Insect Pheromone Biochemistry and Molecular Biology, Elsevier Academic Press, 2003Brownlee, J. Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python, Machine Learning Mastery, 2020Burns, J. A. & Whitesides, G. M. Feed-forward neural networks in chemistry: mathematical systems for classification and pattern recognition, Chemical Reviews, ACS Publications, 1993, 93, 2583-2601Cai, D.; Zhang, C. & He, X. Unsupervised feature selection for multi-cluster data, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, 333-342Cebeci, Z.; Yildiz, F.; Kavlak, A.; Cebeci, C. & Onder, H. ppclust: Probabilistic and Possibilistic Cluster Analysis, R package version 0.1, 2019, 3Chen, R.-C.; Dewi, C.; Huang, S.-W. & Caraka, R. E. Selecting critical features for data classification based on machine learning methods, Journal of Big Data, Springer, 2020, 7, 1-26Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns, Front. Chem., 2019, 7, 809.David, L.; Thakkar, A.; Mercado, R. & Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide, Journal of Cheminformatics, BioMed Central, 2020, 12, 1-22Delgadillo, D. Feromonas. Lo que el viento se llevó, Revista Casa del Tiempo, 2005, 3Dong, J.; Cao, D.-S.; Miao, H.-Y.; Liu, S.; Deng, B.-C.; Yun, Y.-H.; Wang, N.-N.; Lu, A.-P.; Zeng, W.-B. & Chen, A. F. ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation, Journal of cheminformatics, BioMed Central, 2015, 7, 60Effrosynidis, D. & Arampatzis, A. An evaluation of feature selection methods for environmental data, Ecological Informatics, Elsevier, 2021, 61, 101224El-Sayed, A. M. The pherobase: database of insect pheromones and semiochemicals, 2022Ezzat, S. M.; Jeevanandam, J.; Egbuna, C.; Merghany, R. M.; Akram, M.; Daniyal, M.; Nisar, J. & Sharif, A. Semiochemicals: A Green Approach to Pest and Disease Control, Natural Remedies for Pest, Disease and Weed Control, Elsevier, 2020, 81-89Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery Jr., J. A.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B. & Fox, D. J. Gaussian16 Revision C.01 2016Genuer, R.; Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests, Pattern recognition letters, Elsevier, 2010, 31, 2225-2236Géron, A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O'Reilly Media, 2019Ghosh, S. & Dubey, S. K. Comparative analysis of k-means and fuzzy c-means algorithms International, Journal of Advanced Computer Science and Applications, Citeseer, 2013, 4Gitau, C.; Bashford, R.; Carnegie, A. & Gurr, G. A review of semiochemicals associated with bark beetle (Coleoptera: Curculionidae: Scolytinae) pests of coniferous trees: a focus on beetle interactions with other pests and their associates, Forest Ecology and Management, Elsevier, 2013, 297, 1-14Grinberg, M. Flask web development: developing web applications with python, O'Reilly Media, Inc., 2018Janet, J. P. & Kulik, H. J. Machine Learning in Chemistry, American Chemical Society, 2020Jaramillo-Noreña, J.; León, G. D. S.; Rodríguez, V. P.; Garzón, D. Q.; Cuartas, M. Á. Z. & Arroyave, M. G. Capitulo 7: Manejo integrado de plagas, Tecnología para el cultivo de tomate bajo condiciones protegidas, Corporación Colombiana de Investigación Agropecuaria, 2013Judd, W. S.; Campbell, C. S.; Kellogg, E. A.; Stevens, P. F. & Donoghue, M. J. Plant systematics: a phylogenetic approach, Ecología mediterranea, 1999, 25, 215Kalousis, A.; Prados, J. & Hilario, M. Stability of feature selection algorithms: a study on high-dimensional spaces, Knowledge and information systems, Springer, 2007, 12, 95-116Kasabov, N. K. Time-space, spiking neural networks and brain-inspired artificial intelligence, Springer, 2019Kassambara, A. Multivariate Analysis II: Practical Guide to Principal Component Methods in R, Scotts Valley, CA: CreateSpace Independent Publishing Platform.[Google Scholar], 2017Kassambara, A. Practical guide to principal component methods in R: PCA, M (CA), FAMD, MFA, HCPC, factoextra, Sthda, 2017, 2Keany, E. BorutaShap, MIT License, 2021Kohonen, T. Self-organized formation of topologically correct feature maps, Biological cybernetics, Springer, 1982, 43, 59-69Kohonen, T. The self-organizing map, Proceedings of the IEEE, IEEE, 1990, 78, 1464-1480Kramer, O. Scikit-learn, Machine learning for evolution strategies, Springer, 2016, 45-53Kuhn, M. & Johnson, K. Feature engineering and selection: A practical approach for predictive models, CRC Press, 2019Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package, Journal of statistical software, 2010, 36, 1-13Kuzma, T. & Farkaš, I. Embedding Complexity of Learned Representations in Neural Networks, International Conference on Artificial Neural Networks, 2019, 518-528Lal, T. N.; Chapelle, O.; Weston, J. & Elisseeff, A. Embedded methods, Feature extraction, Springer, 2006, 137-165Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions, Advances in neural information processing systems, 2017, 30Maltarollo, V. G.; Honório, K. M. & da Silva, A. B. F. Suzuki, K. (Ed.) Applications of Artificial Neural Networks in Chemical Problems, Chap.10, Artificial Neural Networks, IntechOpen, 2013Mauri, A.; Consonni, V. & Todeschini, R. Molecular descriptors, Springer International Publishing, 2017McHugh, M. L. Interrater reliability: the kappa statistic, Biochemia medica, Medicinska naklada, 2012, 22, 276-282Mishra, S. S.; Shroff, S.; Sahu, J. K.; Naik, P. P. & Baitharu, I. Insect Pheromones and Its Applications in Management of Pest Population, Natural Materials and Products from Insects: Chemistry and Applications, Springer, 2020, 121-136Mitchell, T. M. & others Machine learning, McGraw-hill New York, 1997Moretto Cosson, K. y. A. Pollination of Amorphophallus barthlottii and A. abyssinicus subsp. akeassii (Araceae) by dung beetles (Insecta: Coleoptera: Scarabaeoidea), Association Catharsius, 2019Morgan, E. D. Biosynthesis in insects, Royal society of chemistry, 2010Moriwaki, H.; Tian, Y.-S.; Kawashita, N. & Takagi, T. Mordred: a molecular descriptor calculator, Journal of cheminformatics, BioMed Central, 2018, 10, 1-14Mustaqeem, A.; Anwar, S. M. & Majid, M. Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants, Computational and mathematical methods in medicine, Hindawi, 2018, 2018MySQL, A. MySQL, 2001Odell, S. G.; Lazo, G. R.; Woodhouse, M. R.; Hane, D. L. & Sen, T. Z. The art of curation at a biological database: principles and application, Current Plant Biology, Elsevier, 2017, 11, 2-11Pardo-Locarno, L.; González, J.; Pérez, C.; Yepes, F. & Fernández, C. Escarabajos de importancia agrícola (Coleoptera: Melolonthidae) en la región Caribe colombiana: Registros y propuestas de manejo, Boletín Del Museo Entomológico Francisco Luis Gallejo, 2012, 4, 7-23Peterson, M. A.; Dobler, S.; Larson, E. L.; Juárez, D.; Schlarbaum, T.; Monsen, K. J. & Francke, W. Profiles of cuticular hydrocarbons mediate male mate choice and sexual isolation between hybridising Chrysochus (Coleoptera: Chrysomelidae), Chemoecology, Springer, 2007, 17, 87-96Piñeiro Gomez, J. Diseño de bases de datos relacionales, Ediciones Paraninfo, SA, 2014Pla, L. Análisis multivariado: método de componentes principales, OEA (Organizacion de los Estados Americanos), 1986Raices, M. Comunicación química en larvas de Rhinella arenarum. Caracterización del comportamiento antipredatorio y de las señales de alarma, Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, 2018Raju, M.; Gopi, V. P. & Anitha, V. Multi-class Classification of Alzheimer's Disease using 3DCNN Features and Multilayer Perceptron, 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2021, 368-373Romero-Frías, A.; Murata, Y.; Simões Bento, J. M. & Osorio, C. (1R, 2S, 6R)-Papayanal: a new male-specific volatile compound released by the guava weevil Conotrachelus psidii (Coleoptera: Curculionidae), Bioscience, Biotechnology, and Biochemistry, Oxford University Press, 2016, 80, 848-855Romero-Frías, A. A.; Sinuco, D. C. & Bento, J. M. S. Male-specific volatiles released by the big avocado seed weevil Heilipus lauri Boheman (Coleoptera: Curculionidae), Journal of the Brazilian Chemical Society, SciELO Brasil, 2019, 30, 158-163Romero-López, A. A.; Reyes-Chilpa, R.; Pérez-Flores, F. J.; Lugo-García, G. A. & Maldonado-Rodríguez, J. I. Chemicals in the Genital Chamber of Two Mexican Species of Phyllophaga, Southwestern Entomologist, Society of Southwestern Entomologists, 2019, 44, 457 - 464Samuel, A. L. Some studies in machine learning using the game of checkers, IBM Journal of research and development, IBM, 1959, 3, 210-229Sandri, M. & Zuccolotto, P. Variable selection using random forests, Data analysis, classification and the forward search, Springer, 2006, 263-270Sharma, A.; Sandhi, R. K. & Reddy, G. V. A Review of Interactions between Insect Biological Control Agents and Semiochemicals, Insects, Multidisciplinary Digital Publishing Institute, 2019, 10, 439Shibata, E.; Sato, S.; Sakuratani, Y.; Sugimoto, T.; Kimura, F. & Ito, F. Cerambycid beetles (Coleoptera) lured to chemicals in forests of Nara Prefecture, central Japan, Annals of the Entomological Society of America, Oxford University Press Oxford, UK, 1996, 89, 835-842Silva, W. D.; Millar, J. G.; Hanks, L. M.; Costa, C. M.; Leite, M. O.; Tonelli, M. & Bento, J. M. S. Interspecific cross-attraction between the South American cerambycid beetles Cotyclytus curvatus and Megacyllene acuta is averted by minor pheromone components, Journal of chemical ecology, Springer, 2018, 44, 268-275Smart, L.; Aradottir, G. & Bruce, T. Role of semiochemicals in integrated pest management, Integrated Pest Management, Elsevier, 2014, 93-109Sneath, P. H. Thirty years of numerical taxonomy, Systematic Biology, Society of Systematic Biologists, 1995, 44, 281-298Solorio-Fernández, S.; Carrasco-Ochoa, J. A. & Mart\inez-Trinidad, J. F. A review of unsupervised feature selection methods, Artificial Intelligence Review, Springer, 2020, 53, 907-948Spurlock, J. Bootstrap: responsive web development, O'Reilly Media, Inc., 2013Stanczyk, S.; Champion, B. & Leyton, R. Theory and practice of relational databases, CRC Press, 2003Syakur, M.; Khotimah, B.; Rochman, E. & Satoto, B. D. Integration k-means clustering method and elbow method for identification of the best customer profile cluster, IOP conference series: materials science and engineering, 2018, 336, 012017Tauler, R.; Walczak, B. & Brown, S. D. Comprehensive chemometrics: chemical and biochemical data analysis, Elsevier, 2009Thai, V. D.; Hoan, N. Q. & others Improving Feature Map Quality of SOM Based on Adjusting the Neighborhood Function, International Journal of Computer Science and Information Security, LJS Publishing, 2016, 14, 746Todeschini, R. & Consonni, V. Handbook of molecular descriptors, John Wiley & Sons, 2008Touzet, H. Tree edit distance with gaps, Information Processing Letters, Citeseer, 2003, 85, 123-129Uriarte, E. A. & Mart\in, F. D. Topology preservation in SOM, International journal of applied mathematics and computer sciences, Citeseer, 2005, 1, 19-22Vettigli, G. MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map, 2013Vidal Medina, V. Señales químicas entre el escarabajo-plaga Strategus aloeus (Coleoptera: Scarabaeidae: Dynastinae) y la palma de aceite (Elaeis guineensis Jacq.), Universidad Nacional de Colombia, Universidad Nacional de Colombia, 2021, 120Warner, J. & Sexauer, J. JDWarner/scikit-fuzzy: Scikit-Fuzzy version 0.4. 2 Nov, 2019Wei, J. N.; Duvenaud, D. & Aspuru-Guzik, A. Neural networks for the prediction of organic chemistry reactions, ACS central science, ACS Publications, 2016, 2, 725-732EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unal.edu.co/bitstream/unal/82182/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ORIGINAL1072713817.2022.pdf1072713817.2022.pdfTesis de Maestría en Ciencias Químicaapplication/pdf2251588https://repositorio.unal.edu.co/bitstream/unal/82182/2/1072713817.2022.pdf30e7307fbab695945e9b23201b0468d5MD52THUMBNAIL1072713817.2022.pdf.jpg1072713817.2022.pdf.jpgGenerated Thumbnailimage/jpeg4105https://repositorio.unal.edu.co/bitstream/unal/82182/3/1072713817.2022.pdf.jpg763e350f5949af79c17228adbfebcf8eMD53unal/82182oai:repositorio.unal.edu.co:unal/821822024-08-11 00:59:42.859Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

Clasificación de semioquímicos asociados a coleópteros del suborden Polyphaga mediante redes neuronales artificiales

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