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303 results on '"Serrano, Emilio"'

1. Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networks

Author

Zanardini, Damiano and Serrano, Emilio

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2, I.2.6
Abstract

This paper introduces a new problem in the field of graph mining and social network analysis called new node prediction. More technically, the task can be categorized as zero-shot out-of-graph all-links prediction. This challenging problem aims to predict all links from a new, isolated, and unobserved node that was previously disconnected from the graph. Unlike classic approaches to link prediction (including few-shot out-of-graph link prediction), this problem presents two key differences: (1) the new node has no existing links from which to extract patterns for new predictions; and (2) the goal is to predict not just one, but all the links of this new node, or at least a significant part of them. Experiments demonstrate that an architecture based on Deep Graph Neural Networks can learn to solve this challenging problem in a bibliographic citation network.

Published
2024

2. Unlocking the power of AI models: exploring protein folding prediction through comparative analysis

Author

Tejera-Nevado Paloma, Serrano Emilio, González-Herrero Ana, Bermejo Rodrigo, and Rodríguez-González Alejandro

Subjects
protein structure prediction, protein folding estimation, protein conformation analysis, artificial intelligence models, deep learning models, root-mean-square deviation, Biotechnology, TP248.13-248.65
Abstract

Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated. These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study’s main objective is to assess the accuracy of the model’s predictions, thereby providing insights into the complexities and supporting metrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.

Published
2024
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6. Analysis of the Confidence in the Prediction of the Protein Folding by Artificial Intelligence

Author

Tejera-Nevado, Paloma, Serrano, Emilio, González-Herrero, Ana, Bermejo-Moreno, Rodrigo, Rodríguez-González, Alejandro, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Miguel, editor, Fdez-Riverola, Florentino, editor, Mohamad, Mohd Saberi, editor, and Gil-González, Ana Belén, editor

Published
2023
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11. Unlocking the power of AI models: exploring protein folding prediction through comparative analysis

Author

Ministerio de Ciencia e Innovación (España), Tejera-Nevado, Paloma [0000-0003-0342-6640], Serrano, Emilio [0000-0001-7587-0703], González-Herrero, Ana [0000-0003-2014-563X], Bermejo, Rodrigo [0000-0002-2692-7045], Rodríguez-González, Alejandro [0000-0001-8801-4762], Tejera-Nevado, Paloma, Serrano, Emilio, González-Herrero, Ana, Bermejo, Rodrigo, Rodríguez-González, Alejandro, Ministerio de Ciencia e Innovación (España), Tejera-Nevado, Paloma [0000-0003-0342-6640], Serrano, Emilio [0000-0001-7587-0703], González-Herrero, Ana [0000-0003-2014-563X], Bermejo, Rodrigo [0000-0002-2692-7045], Rodríguez-González, Alejandro [0000-0001-8801-4762], Tejera-Nevado, Paloma, Serrano, Emilio, González-Herrero, Ana, Bermejo, Rodrigo, and Rodríguez-González, Alejandro

Abstract

Protein structure determination has made progress with the aid of deep learning models, enabling the prediction of protein folding from protein sequences. However, obtaining accurate predictions becomes essential in certain cases where the protein structure remains undescribed. This is particularly challenging when dealing with rare, diverse structures and complex sample preparation. Different metrics assess prediction reliability and offer insights into result strength, providing a comprehensive understanding of protein structure by combining different models. In a previous study, two proteins named ARM58 and ARM56 were investigated.These proteins contain four domains of unknown function and are present in Leishmania spp. ARM refers to an antimony resistance marker. The study’s main objective is to assess the accuracy of the model’s predictions,thereby providing insights into the complexities and supportingmetrics underlying these findings. The analysis also extends to the comparison of predictions obtained from other species and organisms. Notably, one of these proteins shares an ortholog with Trypanosoma cruzi and Trypanosoma brucei, leading further significance to our analysis. This attempt underscored the importance of evaluating the diverse outputs from deep learning models, facilitating comparisons across different organisms and proteins. This becomes particularly pertinent in cases where no previous structural information is available.

Published
2024

14. A Moderate Experiential Learning Approach Applied on Data Science

Author

Serrano, Emilio, Manrique, Daniel, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Gennari, Rosella, editor, Vittorini, Pierpaolo, editor, De la Prieta, Fernando, editor, Di Mascio, Tania, editor, Temperini, Marco, editor, Azambuja Silveira, Ricardo, editor, and Ovalle Carranza, Demetrio Arturo, editor

Published
2020
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15. An Ontology-Based Deep Learning Approach for Knowledge Graph Completion with Fresh Entities

Author

Amador-Domínguez, Elvira, Hohenecker, Patrick, Lukasiewicz, Thomas, Manrique, Daniel, Serrano, Emilio, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Herrera, Francisco, editor, Matsui, Kenji, editor, and Rodríguez-González, Sara, editor

Published
2020
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16. An Agent-Based Model for Exploring Pension Law and Social Security Policies

Author

Serrano, Emilio, Satoh, Ken, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sakamoto, Maki, editor, Okazaki, Naoaki, editor, Mineshima, Koji, editor, and Satoh, Ken, editor

Published
2020
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18. An Intelligent and Autoadaptive System of Virtual Identities Based on Deep Learning for the Analysis of Online Advertising Networks

Author

Amador-Domínguez, Elvira, Serrano, Emilio, Mateos-Nobre, Juan David, Ayala-Muñoz, Alfredo, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, De La Prieta, Fernando, editor, González-Briones, Alfonso, editor, Pawleski, Pawel, editor, Calvaresi, Davide, editor, Del Val, Elena, editor, Lopes, Fernando, editor, Julian, Vicente, editor, Osaba, Eneko, editor, and Sánchez-Iborra, Ramón, editor

Published
2019
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23. Analysis of the confidence in the prediction of the protein folding by artificial intelligence

Author

Ministerio de Ciencia e Innovación (España), Tejera-Nevado, Paloma [0000-0003-0342-6640, Serrano, Emilio [0000-0001-7587-0703], González-Herrero, Ana [0000-0003-2014-563X], Bermejo, Rodrigo [0000-0002-2692-7045], Rodríguez-González, Alejandro [0000-0001-8801-4762], Tejera-Nevado, Paloma, Serrano, Emilio, González-Herrero, Ana, Bermejo, Rodrigo, Rodríguez-González, Alejandro, Ministerio de Ciencia e Innovación (España), Tejera-Nevado, Paloma [0000-0003-0342-6640, Serrano, Emilio [0000-0001-7587-0703], González-Herrero, Ana [0000-0003-2014-563X], Bermejo, Rodrigo [0000-0002-2692-7045], Rodríguez-González, Alejandro [0000-0001-8801-4762], Tejera-Nevado, Paloma, Serrano, Emilio, González-Herrero, Ana, Bermejo, Rodrigo, and Rodríguez-González, Alejandro

Abstract

The determination of protein structure has been facilitated using deep learning models, which can predict protein folding from protein sequences. In some cases, the predicted structure can be compared to the already-known distribution if there is information from classic methods such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, or electron microscopy (EM). However, challenges arise when the proteins are not abundant, their structure is heterogeneous, and protein sample preparation is difficult. To determine the level of confidence that supports the prediction, different metrics are provided. These values are important in two ways: they offer information about the strength of the result and can supply an overall picture of the structure when different models are combined. This work provides an overview of the different deep-learning methods used to predict protein folding and the metrics that support their outputs. The confidence of the model is evaluated in detail using two proteins that contain four domains of unknown function.

Published
2023

25. Predicting the risk of suffering chronic social exclusion with machine learning

Author

Serrano, Emilio, del Pozo-Jiménez, Pedro, Suárez-Figueroa, Mari Carmen, González-Pachón, Jacinto, Bajo, Javier, Gómez-Pérez, Asunción, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Omatu, Sigeru, editor, Rodríguez, Sara, editor, Villarrubia, Gabriel, editor, Faria, Pedro, editor, Sitek, Paweł, editor, and Prieto, Javier, editor

Published
2018
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28. A Multi-agent Architecture for Labeling Data and Generating Prediction Models in the Field of Social Services

Author

Serrano, Emilio, del Pozo-Jiménez, Pedro, Suárez-Figueroa, Mari Carmen, González-Pachón, Jacinto, Bajo, Javier, Gómez-Pérez, Asunción, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Bajo, Javier, editor, Vale, Zita, editor, Hallenborg, Kasper, editor, Rocha, Ana Paula, editor, Mathieu, Philippe, editor, Pawlewski, Pawel, editor, Del Val, Elena, editor, Novais, Paulo, editor, Lopes, Fernando, editor, Duque Méndez, Nestor D., editor, Julián, Vicente, editor, and Holmgren, Johan, editor

Published
2017
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29. Towards Social Care Prediction Services Aided by Multi-agent Systems

Author

Serrano, Emilio, Bajo, Javier, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Montagna, Sara, editor, Abreu, Pedro Henriques, editor, Giroux, Sylvain, editor, and Schumacher, Michael Ignaz, editor

Published
2017
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35. Técnicas de composición basadas en fractales

Author

Sánchez Serrano, Emilio, Navarro, Juan F., and Universidad de Alicante. Departamento de Matemática Aplicada

Subjects
Música fractal, Algoritmo, Dimensión, Fractales, Geometría
Abstract

La geometría fractal es una poderosa herramienta para describir el mundo que nos rodea, así como el origen de numerosas aplicaciones entre las que encontramos la música fractal. En este proyecto se estudia la geometría fractal de manera formal con el objetivo de servir como base de estudio a cualquier entendido en la materia. Además, se construyen algunos de los fractales más importantes como son el conjunto de Cantor, la curva de Koch y el triángulo de Sierpinski. Posteriormente, se aplican estos objetos geométricos a la creación de piezas musicales, es decir, a la música fractal. Para ello, se han usado principalmente dos metodologías que son el programa Fractmus 2000 y la implementación de algoritmos propios. Estas ideas deben ser el punto de partida para cualquier investigador que quiera explorar este fascinante género, descubierto en obras de compositores del calibre de Bach o Beethoven.

Published
2023

36. An Integrated Framework for Enabling End-User Configuration of AmI Simulations for Open Wide Locations

Author

Alcarria, Ramón, Serrano, Emilio, Sanz, Jorge Gómez, Fernández, Alberto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, García-Chamizo, Juan M., editor, Fortino, Giancarlo, editor, and Ochoa, Sergio F., editor

Published
2015
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37. A Survey of Twitter Rumor Spreading Simulations

Author

Serrano, Emilio, Iglesias, Carlos A., Garijo, Mercedes, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Núñez, Manuel, editor, Nguyen, Ngoc Thanh, editor, Camacho, David, editor, and Trawiński, Bogdan, editor

Published
2015
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38. Técnicas de composición basadas en fractales

Author

Navarro, Juan F., Universidad de Alicante. Departamento de Matemática Aplicada, Sánchez Serrano, Emilio, Navarro, Juan F., Universidad de Alicante. Departamento de Matemática Aplicada, and Sánchez Serrano, Emilio

Abstract

La geometría fractal es una poderosa herramienta para describir el mundo que nos rodea, así como el origen de numerosas aplicaciones entre las que encontramos la música fractal. En este proyecto se estudia la geometría fractal de manera formal con el objetivo de servir como base de estudio a cualquier entendido en la materia. Además, se construyen algunos de los fractales más importantes como son el conjunto de Cantor, la curva de Koch y el triángulo de Sierpinski. Posteriormente, se aplican estos objetos geométricos a la creación de piezas musicales, es decir, a la música fractal. Para ello, se han usado principalmente dos metodologías que son el programa Fractmus 2000 y la implementación de algoritmos propios. Estas ideas deben ser el punto de partida para cualquier investigador que quiera explorar este fascinante género, descubierto en obras de compositores del calibre de Bach o Beethoven.

Published
2023

40. Creating and Validating Emergency Management Services by Social Simulation and Semantic Web Technologies

Author

Poveda, Geovanny, Serrano, Emilio, Garijo, Mercedes, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hervás, Ramón, editor, Lee, Sungyoung, editor, Nugent, Chris, editor, and Bravo, José, editor

Published
2014
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41. Ambient Intelligence Services Personalization via Social Choice Theory

Author

Serrano, Emilio, Moncada, Pablo, Garijo, Mercedes, Iglesias, Carlos A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hervás, Ramón, editor, Lee, Sungyoung, editor, Nugent, Chris, editor, and Bravo, José, editor

Published
2014
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42. A multi-agent architecture for mobile sensing systems

Author

Laport, Francisco, Serrano, Emilio, and Bajo, Javier

Abstract

Mobile sensing systems based on smartphones, connected vehicles and integrated sensors on new mobile devices have become an important alternative for the development of intelligent services in large urban environments. Massive data collection and its real-time analysis are essential for big cities to move towards energy efficiency, sustainable mobility, protection of the environment and economic sustainability. Current research and applications are mainly focused on the use of individual devices and the analysis of information on a single domain (e.g. activity recognition). However, it is still necessary to provide solutions for social problems based on smart mobile devices connected to the city. In this paper, we present an architecture for mobile sensing systems in large cities based on the intelligent agent paradigm and multi-agent systems. The presented platform provides support for multi-purpose machine learning services, implementing expert learning agents in each domain where the system collects data. Furthermore, the main challenges in mobile sensing systems such as scalability in crowded environments, handling of a large amount of data and the increasing appearance of sensing devices are addressed by the architecture due to the agent paradigm and multi-agent systems suit these demands naturally.

Published
2024
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49. Relación entre la investigación educativa, la sociedad y la producción de conocimientos

Author

Rojas Raya, Aketzali, additional, Jiménez García, Martha, additional, Martínez Ortega, Ma. de los Ángeles, additional, Camacho Rodríguez, Fabiola Elisa, additional, Chapa Chapa, Mireya, additional, Talavera Chávez, Raquel, additional, Plazola Rivera, María Soledad, additional, Berrelleza Carrillo, Marianna, additional, Ojeda Orta, María Elizabeth, additional, Maya Palacios, Edgar Uxmal, additional, Flores Fahara, Manuel, additional, Martínez Ortiz, María Guadalupe, additional, Rueda Ventura, Elsa, additional, Gómez Lemus, Marbella Araceli, additional, Pérez Acosta, Candelaria, additional, Gómez Sánchez, David, additional, Recio Reyes, Ramón Gerardo, additional, López Gama, Héctor, additional, Martínez López, Eugenia Inés, additional, Sánchez Domínguez, Juan Pablo, additional, Cervantes García, Francisco, additional, Guzmán Castañeda, Adriana, additional, Durán Padilla, Minaksi Elidé, additional, Contreras Guerrero, Edgar Manuel, additional, Rizo Ruiz, Ángel Adolfo, additional, Sosa Morales, Pedro Addam, additional, Jiménez Villacís, Marcos Gesiel, additional, Caballero Pool, Alejandro Israel, additional, Lugo Galán, Guadalupe, additional, Alzúa Ramírez, Víctor Hugo, additional, Fabián Lagunas, Andrea, additional, Ruiz Camacho, Raquel, additional, Soto Ríos, María de Lourdes, additional, Serrano Barquín, Héctor, additional, Ruiz Serrano, Emilio, additional, Serrano Barquín, Carolina, additional, Zarza Delgado, Martha Patricia, additional, Fuentes Mata, Irma, additional, Parga Parga, Pablo, additional, Torres González, Claudia, additional, Rodríguez Pérez, Ivonne, additional, Román Fuentes, Juan Carlos, additional, Badillo Islas, Luis Felipe, additional, Rodríguez Aguilar, Edgar, additional, López Pérez, Christian Natalie, additional, Nava Paredes, Alma Valeria, additional, Verástica Cháidez, María Luisa Guadalupe, additional, Salazar González, Crisanto, additional, Zamora De León, Lucía, additional, Landín González, Talina Aurora, additional, Ovalle Perales, Francisco, additional, Hernández Horta, Ingrid Anai, additional, Fabila Castillo, Luis Humberto, additional, Ovalle Osuna, Oscar Omar, additional, Hernández Solís, Silvia, additional, Apodaca del Ángel, Lourdes Evelyn, additional, Isaac Márquez, Ricardo, additional, Martínez Flores, Fernanda Gabriela, additional, Delgado Sánchez, Ulises, additional, and Flores Saucedo, Martina Patricia, additional

Published
2017
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