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1. Statistical Learning Analysis of Thyroid Cancer Microarray Data

Author

Petrini, Iván, Cecchini, Rocío L., Mascaró, Marilina, Ponzoni, Ignacio, Carballido, Jessica A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2022
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3. PreCLAS: An Evolutionary Tool for Unsupervised Feature Selection

Author

Carballido, Jessica A., Ponzoni, Ignacio, Cecchini, Rocío L., 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, de la Cal, Enrique Antonio, editor, Villar Flecha, José Ramón, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2020
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7. Hemoxygenase-1 Promotes Head and Neck Cancer Cell Viability

Author

Mascaró, Marilina, primary, Alonso, Exequiel G., additional, Schweitzer, Karen, additional, Rabassa, Martín E., additional, Carballido, Jessica A., additional, Ibarra, Agustina, additional, Alonso, Eliana N., additional, Bermúdez, Vicente, additional, Fernández Chavez, Lucía, additional, Coló, Georgina P., additional, Ferronato, María Julia, additional, Pichel, Pamela, additional, Recio, Sergio, additional, Clemente, Valentina, additional, Fermento, Maria Eugenia, additional, Facchinetti, María Marta, additional, and Curino, Alejandro C., additional

Published
2022
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9. BAT: A New Biclustering Analysis Toolbox

Author

Gallo, Cristian Andrés, Dussaut, Julieta Sol, Carballido, Jessica Andrea, Ponzoni, Ignacio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Ferreira, Carlos E., editor, Miyano, Satoru, editor, and Stadler, Peter F., editor

Published
2010
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10. BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering

Author

Gallo, Cristian Andrés, Carballido, Jessica Andrea, Ponzoni, Ignacio, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael S., editor, Guimarães, Katia S., editor, Panchenko, Anna, editor, and Przytycka, Teresa M., editor

Published
2009
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11. Bus Network Scheduling Problem: GRASP + EAs with PISA * Simulation

Author

Olivera, Ana C., Frutos, Mariano, Carballido, Jessica A., Ponzoni, Ignacio, Brignole, Nélida B., 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Cabestany, Joan, editor, Sandoval, Francisco, editor, Prieto, Alberto, editor, and Corchado, Juan M., editor

Published
2009
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12. Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform

Author

Gallo, Cristian Andrés, Carballido, Jessica Andrea, Ponzoni, Ignacio, 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, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Pizzuti, Clara, editor, Ritchie, Marylyn D., editor, and Giacobini, Mario, editor

Published
2009
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13. Papillary Thyroid Carcinoma: A thorough Bioinformatic Analysis of Gene Expression and Clinical Data.

Author

Petrini, Iván, Cecchini, Rocío L., Mascaró, Marilina, Ponzoni, Ignacio, and Carballido, Jessica A.

Subjects
GENE expression, PAPILLARY carcinoma, THYROID cancer, GENETIC markers, PROTEIN-protein interactions, GENE expression profiling
Abstract

The likelihood of being diagnosed with thyroid cancer has increased in recent years; it is the fastest-expanding cancer in the United States and it has tripled in the last three decades. In particular, Papillary Thyroid Carcinoma (PTC) is the most common type of cancer affecting the thyroid. It is a slow-growing cancer and, thus, it can usually be cured. However, given the worrying increase in the diagnosis of this type of cancer, the discovery of new genetic markers for accurate treatment and prognostic is crucial. In the present study, the aim is to identify putative genes that may be specifically relevant in PTC through bioinformatic analysis of several gene expression public datasets and clinical information. Two datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) dataset were studied. Statistics and machine learning methods were sequentially employed to retrieve a final small cluster of genes of interest: PTGFR, ZMAT3, GABRB2, and DPP6. Kaplan–Meier plots were employed to assess the expression levels regarding overall survival and relapse-free survival. Furthermore, a manual bibliographic search for each gene was carried out, and a Protein–Protein Interaction (PPI) network was built to verify existing associations among them, followed by a new enrichment analysis. The results revealed that all the genes are highly relevant in the context of thyroid cancer and, more particularly interesting, PTGFR and DPP6 have not yet been associated with the disease up to date, thus making them worthy of further investigation as to their relationship to PTC. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Using Computational Intelligence and Parallelism to Solve an Industrial Design Problem

Author

Asteasuain, Fernando, Carballido, Jessica A., Vazquez, Gustavo E., Ponzoni, Ignacio, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Sichman, Jaime Simão, editor, Coelho, Helder, editor, and Rezende, Solange Oliveira, editor

Published
2006
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15. A Novel Application of Evolutionary Computing in Process Systems Engineering

Author

Carballido, Jessica Andrea, Ponzoni, Ignacio, Brignole, Nélida Beatriz, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Raidl, Günther R., editor, and Gottlieb, Jens, editor

Published
2005
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17. On Stopping Criteria for Genetic Algorithms

Author

Safe, Martín, Carballido, Jessica, Ponzoni, Ignacio, Brignole, Nélida, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Bazzan, Ana L. C., editor, and Labidi, Sofiane, editor

Published
2004
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19. Filtering non-balanced data using an evolutionary approach.

Author

Carballido, Jessica A, Ponzoni, Ignacio, and Cecchini, Rocío L

Subjects
GENE expression, BIG data, DATA analysis, EVOLUTIONARY algorithms
Abstract

Matrices that cannot be handled using conventional clustering, regression or classification methods are often found in every big data research area. In particular, datasets with thousands or millions of rows and less than a hundred columns regularly appear in biological so-called omic problems. The effectiveness of conventional data analysis approaches is hampered by this matrix structure, which necessitates some means of reduction. An evolutionary method called PreCLAS is presented in this article. Its main objective is to find a submatrix with fewer rows that exhibits some group structure. Three stages of experiments were performed. First, a benchmark dataset was used to assess the correct functionality of the method for clustering purposes. Then, a microarray gene expression data matrix was used to analyze the method's performance in a simple classification scenario, where differential expression was carried out. Finally, several classification methods were compared in terms of classification accuracy using an RNA-seq gene expression dataset. Experiments showed that the new evolutionary technique significantly reduces the number of rows in the matrix and intelligently performs unsupervised row selection, improving classification and clustering methods. [ABSTRACT FROM AUTHOR]

Published
2023
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25. List of contributors

Author

Ahangar-Asr, A., primary, Alavi, Amir Hossein, additional, Ayvaz, M. Tamer, additional, Azamathulla, H. Md, additional, Bazaz, Jafar Bolouri, additional, Carballido, Jessica Andrea, additional, Cheng, Y.M., additional, Chua, C.G., additional, Das, Sarat Kumar, additional, Elçi, Alper, additional, Faramarzi, A., additional, Frutos, Mariano, additional, Gandomi, Amir Hossein, additional, Geem, Z.W., additional, Goh, Anthony T.C., additional, Hassanzadeh, Yousef, additional, Jain, Sharad Kumar, additional, Javadi, A.A., additional, Jha, Manoj K., additional, Kallioras, Nikos, additional, Karlaftis, Matthew G., additional, Kesur, Khewal Bhupendra, additional, Lagaros, Nikos D., additional, Mollahasani, Ali, additional, Mottaghifard, N., additional, Nejad, Fereidoon Moghadas, additional, Nikjoofar, Ali, additional, Olivera, Ana Carolina, additional, Perumal, Muthiah, additional, Piliounis, George, additional, Rani, Deepti, additional, Samui, Pijush, additional, Shahin, Mohamed A., additional, Singh, Vijay P., additional, Srivastava, Dinesh Kumar, additional, Talatahari, Siamak, additional, Vasan, A., additional, Yang, Xin-She, additional, Zarghami, Mahdi, additional, and Zakeri, Hamzeh, additional

Published
2013
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36. Discovering time-lagged rules from microarray data using gene profile classifiers

Author

Ponzoni Ignacio, Carballido Jessica A, and Gallo Cristian A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Gene regulatory networks have an essential role in every process of life. In this regard, the amount of genome-wide time series data is becoming increasingly available, providing the opportunity to discover the time-delayed gene regulatory networks that govern the majority of these molecular processes. Results This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations. Conclusions A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.

Published
2011
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41. On Artificial Gene Regulatory Networks

Author

Carballido, Jessica Andrea and Ponzoni, Ignacio

Subjects
Ingeniería de Sistemas y Comunicaciones, purl.org/becyt/ford/2 [https], Bioinformatics, Gene Regulatory Networks, INGENIERÍAS Y TECNOLOGÍAS, purl.org/becyt/ford/2.2 [https], Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información, Artificial Grns
Abstract

Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used to build artificial GRNs. Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur; Argentina

Published
2008

42. On Artificial Gene Regulatory Networks

Author

Carballido, Jessica A. and Ponzoni, Ignacio

Subjects
Bioinformatics, Ciencias Informáticas, Gene Regulatory Networks, Artificial GRNs
Abstract

Gene regulatory networks (GRNs) represent dependencies between genes and their products during protein synthesis at the molecular level. At the present there exist many inference methods that infer GRNs form observed data. However, gene expression data sets have in general considerable noise that make understanding and learning even simple regulatory patterns difficult. Also, there is no well-known method to test the accuracy of inferred GRNs. Given these drawbacks, characterizing the effectiveness of different techniques to uncover gene networks remains a challenge. The development of artificial GRNs with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of those techniques. In this work we introduce this problem in terms of machine learning and present a review of the main formalisms that have been used, Sociedad Argentina de Informática e Investigación Operativa

Published
2008

43. Bus Network Optimization with a Time-Dependent Hybrid Algorithm

Author

Olivera, Ana, Frutos, Mariano, Carballido, Jessica, and Brignole, Nélida

Subjects
Bus Network Scheduling Problem, optimization, hybrid genetic algorithms
Abstract

This paper describes a new hybrid technique that combines a Greedy Randomized Adaptive Search Procedure (GRASP) and a genetic algorithm with simulation features in order to solve the Bus-Network Scheduling Problem (BNSP). The GRASP is used as an initialization method to find the routes between bus stops. T he Genetic Algorithm is used to find the whole configuration of the bus network, together with a simulation tool that finds the values of the environmentally dependent dynamic variables. The new method was tested with an academic case of study, and the results clearly satisfy the requirements of both the transport user and the transport operator.

Published
2008
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44. pdAGMO para configuración inicial de sensores en procesos industriales

Author

Asteasuain, Fernando, Carballido, Jessica Andrea, Vazquez, Gustavo Esteban, Ponzoni, Ignacio, and Brignole, Nélida Beatriz

Subjects
Ingeniería de Sistemas y Comunicaciones, Instrumentación, purl.org/becyt/ford/2 [https], Algoritmos Genéticos Multiobjetivo, Procesamiento Paralelo, purl.org/becyt/ford/2.2 [https], PVM, INGENIERÍAS Y TECNOLOGÍAS, Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información, Ingeniería de Procesos
Abstract

En este trabajo se presenta una implementación paralelo-distribuida de un algoritmo genético multiobjetivo (pdAGMO), desarrollado para efectuar la selección de la configuración inicial de sensores en el diseño de instrumentación de plantas de procesos. El pdAGMO fue diseñado empleando el modelo evolutivo de islas y el paradigma masterworker, mientras que para su implementación se empleó la librería de pasaje de mensajes PVM (Parallel Virtual Machine). El desempeño del pdAGMO fue evaluado a través de su aplicación a un caso de estudio industrial correspondiente a una planta de producción de amoníaco. Los resultados alcanzados son muy satisfactorios en términos de speed-up, eficiencia y calidad del diseño de instrumentación. Fil: Asteasuain, Fernando. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina

Published
2004

45. Initial Sensor Network Design with a Multi-Objective Genetic Algorithm

Author

Carballido, Jessica Andrea, Ponzoni, Ignacio, and Brignole, Nélida B.

Subjects
Multi-Objective Optimization, Ciencias Informáticas, Sensor Network Design, Genetic algorithms
Abstract

A Multi-Objective Genetic Algorithm (MOGA) application, which is based on the aggregating approach, is proposed in this article. Its aim is to find a consistent instrument configuration for industrial process plants that will constitute a convenient initial set of input data for structural Observability Analysis Algorithms (OAs). The better this configuration is, the faster the OAs will converge to a satisfactory solution. Algorithmic effectiveness was evaluated through the analysis of small academic case studies. The results obtained through our algorithm show excellent performance. Therefore, it can be stated that the prototype presented in this work is good enough to serve as a sound basis for the development of the definitive MOGA module, whose implementation will support large-size industrial plant models., Sociedad Argentina de Informática e Investigación Operativa

Published
2004

46. The Software Architecture of a Decision Support System for Process Plant Instrumentation

Author

Vazquez, Gustavo Esteban, Ferraro, Sebastián José, Carballido, Jessica Andrea, Ponzoni, Ignacio, Sanchez, Mabel Cristina, and Brignole, Nélida Beatriz

Subjects
Ingeniería Química, OBSERVABILITY, purl.org/becyt/ford/2.4 [https], purl.org/becyt/ford/2 [https], Otras Ingeniería Química, INSTRUMENTATION, INGENIERÍAS Y TECNOLOGÍAS, DECISION SUPPORT SYSTEM, REDUNDANCY, SOFTWARE ENGINEERING
Abstract

In this work we present the framework of a new software package to carry out the classification of the state variables in a process plant in order to determine the most convenient instrumentation configuration on the basis of adequate mathematical models of plant behaviour at steady state. We have designed and developed robust and efficient techniques for each step of the categorization task, the key modules being those for observability and redundancy analysis. The individual performance of each module with both academic and industrial examples was successful, revealing comparable or even better results than other existing techniques. Key aspects, such as modularity, user-friendliness, reliability, flexibility, expansibility, functional scalability, and stand-alone capabilities, were taken into account for the design of the integrated software. Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Ferraro, Sebastián José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina Fil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina

Published
2003

47. Discretization of gene expression data revised.

Author

Gallo, Cristian A., Cecchini, Rocio L., Carballido, Jessica A., Micheletto, Sandra, and Ponzoni, Ignacio

Subjects
DISCRETIZATION methods, COMPUTATIONAL fluid dynamics, GENE expression, MOLECULAR genetics, DEVELOPMENTAL stability (Genetics)
Abstract

Gene expression measurements represent the most important source of biological data used to unveil the interaction and functionality of genes. In this regard, several data mining and machine learning algorithms have been proposed that require, in a number of cases, some kind of data discretization to perform the inference. Selection of an appropriate discretization process has a major impact on the design and outcome of the inference algorithms, as there are a number of relevant issues that need to be considered. This study presents a revision of the current state-of-the-art discretization techniques, together with the key subjects that need to be considered when designing or selecting a discretization approach for gene expression data. [ABSTRACT FROM AUTHOR]

Published
2016
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50. Using Computational Intelligence and Parallelism to Solve an Industrial Design Problem.

Author

Sichman, Jaime Simão, Coelho, Helder, Rezende, Solange Oliveira, Asteasuain, Fernando, Carballido, Jessica A., Vazquez, Gustavo E., and Ponzoni, Ignacio

Abstract

In this work we present a critical analysis of three novel parallel-distributed implementations of a multi-objective genetic algorithm (pdGAs) for instrumentation design applications. The pdGAs aim at establishing a sensible configuration of sensors for the initialization of instrumentation design studies of industrial processes. They were built on the basis of an evolutionary island model, the master-worker paradigm, and different migration and parameter control policies. The performance of the resulting implementations was assessed by testing algorithmic behavior on an industrial example that corresponds to an ammonia synthesis plant. The three pdGAs' results were highly satisfactory in terms of speed-up, efficiency and instrumentation quality, thus revealing to constitute competitive tools with strong potential for their use in the industrial area. As well, from an overall point of view, the pdGA version with adaptive parameter control represents the best implementation's alternative. Keywords: Genetic Algorithms, Distributed Computing, Instrumentation Design. [ABSTRACT FROM AUTHOR]

Published
2006
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