Georgina Stegmayer

发表

Diversity control for improving the analysis of consensus clustering

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2016, Inf. Sci..

Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2018, Bioinform..

EvoMS: An evolutionary tool to find de novo metabolic pathways

Georgina Stegmayer, Diego H. Milone, Matias Gerard, 2015, Biosyst..

Métodos de agrupamiento no supervisado para la integración de datos genómicos y metabólicos de múltiples líneas de introgresión

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2009, Inteligencia Artif..

Artificial Life Contest - A Tool for Informal Teaching of Artificial Intelligence

Georgina Stegmayer, Diego H. Milone, Daniel Beber, 2009, CSEDU.

A Novel Method to Control the Diversity in Cluster Ensembles

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2013 .

Neural network model for integration and visualization of introgressed genome and metabolite data

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2009, 2009 International Joint Conference on Neural Networks.

A Neural Network that helps building a Nonlinear Dynamical model of a Power Amplifier

Georgina Stegmayer, Omar Chiotti, Giancarlo Orengo, 2005, ESANN.

Complexity measures of the mature miRNA for improving pre-miRNAs prediction

Georgina Stegmayer, Diego H. Milone, Jonathan Raad, 2019, Bioinform..

Genome-wide pre-miRNA discovery from few labeled examples

Georgina Stegmayer, Diego H. Milone, Cristian A. Yones, 2018, Bioinform..

Knowledge Source Discovery: An Experience Using Ontologies, WordNet and Artificial Neural Networks

Georgina Stegmayer, M. L. Caliusco, María Laura Caliusco, 2009, KES.

ANALYSIS AND INTEGRATION OF BIOLOGICAL DATA: A DATA MINING APPROACH USING NEURAL NETWORKS

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2011 .

Predicting protein functions with deep learning and multi-source data

Georgina Stegmayer, Diego H. Milone, Rabie Saidi, 2020 .

ANN-Agent for Distributed Knowledge Source Discovery

Georgina Stegmayer, Omar Chiotti, M. L. Caliusco, 2007, OTM Workshops.

A Method to Improve the Analysis of Cluster Ensembles

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2014, Inteligencia Artif..

Comparison of Volterra Models Extracted from a Neural Network for Nonlinear Systems Modeling

Georgina Stegmayer, 2005, ICANN.

Estimation of the Particle Size Distribution of a Latex using a General Regression Neural Network

Georgina Stegmayer, Omar Chiotti, Jorge R. Vega, 2008, IFIP AI.

Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics

Georgina Stegmayer, Diego H. Milone, Diego H Milone, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Extreme learning machines for reverse engineering of gene regulatory networks from expression time series

Georgina Stegmayer, Diego H. Milone, Mariano Rubiolo, 2018, Bioinform..

Compressing arrays of classifiers using Volterra-neural network: application to face recognition

Georgina Stegmayer, Diego H. Milone, Mariano Rubiolo, 2012, Neural Computing and Applications.

Data Mining Over Biological Datasets: An Integrated Approach Based on Computational Intelligence

Georgina Stegmayer, Diego H. Milone, Matias Gerard, 2012, IEEE Computational Intelligence Magazine.

Discovering network relations in big time series with application to bioinformatics

Georgina Stegmayer, Diego H. Milone, Mariano Rubiolo, 2015 .

Improving clustering with metabolic pathway data

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2014, BMC Bioinformatics.

ASAI - Guest Editorial

Georgina Stegmayer, Marcelo A. Falappa, 2012, Inteligencia Artif..

*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2010, BMC Bioinformatics.

An evolutionary approach to metabolic pathways search

Georgina Stegmayer, Diego H. Milone, Matias Gerard, 2012, Inteligencia Artif..

Clustering biological data with SOMs: On topology preservation in non-linear dimensional reduction

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2013, Expert Syst. Appl..

miRNAfe: A comprehensive tool for feature extraction in microRNA prediction

Georgina Stegmayer, Diego H. Milone, Laura Kamenetzky, 2015, Biosyst..

Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition

Georgina Stegmayer, Diego H. Milone, César Ernesto Martínez, 2009, Inteligencia Artif..

Novel Neural Network application to nonlinear electronic devices: building a Volterra series model

Georgina Stegmayer, Omar Chiotti, 2005, Inteligencia Artif..

DL4papers: a deep learning approach for the automatic interpretation of scientific articles

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2020, Bioinform..

Semantic Web Technologies and Artificial Neural Networks for Intelligent Web Knowledge Source Discovery

Georgina Stegmayer, M. L. Caliusco, María Laura Caliusco, 2010 .

Knowledge Component of a Multiagent Distributed Decision Support System

Georgina Stegmayer, Omar Chiotti, M. L. Caliusco, 2002, JIISIC.

Stability analysis in overlapped clusters

Georgina Stegmayer, Diego H. Milone, David Nazareno Campo, 2014, Inteligencia Artif..

An evolutionary approach for searching metabolic pathways

Georgina Stegmayer, Diego H. Milone, Matias Gerard, 2013, Comput. Biol. Medicine.

Predicting novel microRNA: a comprehensive comparison of machine learning approaches

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2019, Briefings Bioinform..

Neural Networks applied to wireless communications

Georgina Stegmayer, Omar Chiotti, 2006, IFIP AI.

Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

Georgina Stegmayer, M. L. Caliusco, María Laura Caliusco, 2012, Inf. Sci..

Automatic recognition of quarantine citrus diseases

Georgina Stegmayer, Diego H. Milone, Sergio Garran, 2013, Expert Syst. Appl..

Volterra NN-based behavioral model for new wireless communications devices

Georgina Stegmayer, Omar Chiotti, G. Stegmayer, 2009, Neural Computing and Applications.

A Biologically Inspired Validity Measure for Comparison of Clustering Methods over Metabolic Data Sets

Georgina Stegmayer, Diego H. Milone, Fernando Carrari, 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data

Pablo M. Granitto, Georgina Stegmayer, Diego H. Milone, 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Extreme learning machine prediction under high class imbalance in bioinformatics

Georgina Stegmayer, Diego H. Milone, Leandro E. Di Persia, 2017, 2017 XLIII Latin American Computer Conference (CLEI).

Evolutionary algorithm for metabolic pathways synthesis

Georgina Stegmayer, Diego H. Milone, Matias Gerard, 2016, Biosyst..

Identification of Frequency-Domain Volterra Model Using Neural Networks

Georgina Stegmayer, Omar Chiotti, 2005, ICANN.

Neural Networks and Volterra series for modeling new wireless communication devices

Georgina Stegmayer, G. Stegmayer, 2007, 2007 International Joint Conference on Neural Networks.

Compressing a neural network classifier using a Volterra-Neural Network model

Georgina Stegmayer, Diego H. Milone, Mariano Rubiolo, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

How to Develop Intelligent Agents in an Easy Way with FAIA

Milton Pividori, Georgina Stegmayer, Jorge Roa, 2010 .

High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM

Georgina Stegmayer, Diego H. Milone, Laura Kamenetzky, 2017, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

A novel clustering approach for biological data using a new distance based on Gene Ontology

Pablo M. Granitto, Georgina Stegmayer, Diego H. Milone, 2013 .

A very simple and fast way to access and validate algorithms in reproducible research

Milton Pividori, Georgina Stegmayer, Diego H. Milone, 2016, Briefings Bioinform..

Mining Gene Regulatory Networks by Neural Modeling of Expression Time-Series

Georgina Stegmayer, Diego H. Milone, Mariano Rubiolo, 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning

Georgina Stegmayer, Diego H. Milone, Diego H Milone, 2020, Briefings Bioinform..

HextractoR: an R package for automatic extraction of hairpins from genome-wide data

Georgina Stegmayer, Laura Kamenetzky, Cristian Yones, 2020 .

Improving clustering with metabolic pathway

Georgina Stegmayer, Fernando Carrari, M ariana López, 2014 .

miRNAss: a semi-supervised approach for microRNA prediction

Georgina Stegmayer, Diego H. Milone, Cristian Yones, 2015 .

Computational Prediction of Novel miRNAs from Genome-Wide Data.

Georgina Stegmayer, Laura Kamenetzky, Cristian Yones, 2017, Methods in molecular biology.

Metabolic pathways synthesis based on ant colony optimization

Georgina Stegmayer, Diego H. Milone, Diego H Milone, 2018, Scientific Reports.

Industrial SBR Process: Computer Simulation Study for Online Estimation of Steady‐State Variables Using Neural Networks

Georgina Stegmayer, Luis Marcelino Gugliotta, G. Stegmayer, 2007 .

A Neural Network Model for Estimating the Particle Size Distribution of Dilute Latex from Multiangle Dynamic Light Scattering Measurements

Georgina Stegmayer, Jorge R. Vega, Luis A. Clementi, 2009 .