G. Stegmayer

发表

Novel SARS-CoV-2 encoded small RNAs in the passage to humans

Diego H. Milone, F. Ariel, G. Merino, 2020, Bioinform..

MicroRNA discovery in the human parasite Echinococcus multilocularis from genome-wide data.

Diego H. Milone, C. Yones, L. Kamenetzky, 2016, Genomics.

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

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

Neural Networks and Wireless Communications

G. Stegmayer, O. Chiotti, M. Rubiolo, 2010 .

Modeling New Generation Wireless Communications devices with Neural Networks

G. Stegmayer, O. Chiotti, 2005 .

Whole genome analysis of codon usage in Echinococcus.

Diego H. Milone, L. Kamenetzky, G. Stegmayer, 2018, Molecular and biochemical parasitology.

Metabolic analyses of interspecific tomato recombinant inbred lines for fruit quality improvement

Diego H. Milone, Mariana G. Lopez, M. Zanor, 2015, Metabolomics.

Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks

G. Stegmayer, A. Edera, E. Fenoy, 2022, Briefings Bioinform..

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

G. Stegmayer, 2005, ICANN.

Genome-wide hairpins datasets of animals and plants for novel miRNA prediction

Diego H. Milone, J. Raad, L. Bugnon, 2019, Data in brief.

Alternative use of miRNA-biogenesis co-factors in plants at low temperatures

Diego H. Milone, C. Yones, G. Stegmayer, 2019, Development.

Volterra NN-based behavioral model for new wireless communications devices

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

Neural networks and volterra series for time-domain PA behavioral models

G. Ghione, M. Pirola, F. Giannini, 2007 .

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

G. Stegmayer, O. Chiotti, 2005 .

A Method to Improve the Analysis of Cluster Ensembles

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

A Novel Method to Control the Diversity in Cluster Ensembles

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

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.

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

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

*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.

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.

Deep Learning for the discovery of new pre-miRNAs: Helping the fight against COVID-19

Diego H. Milone, F. Ariel, G. Merino, 2021, Machine Learning with Applications.

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.

Hierarchical deep learning for predicting GO annotations by integrating protein knowledge

Diego H. Milone, G. Merino, G. Stegmayer, 2022, Bioinform..

Improving clustering with metabolic pathway

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

Improving clustering with metabolic pathway data

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

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

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

Highlights of the 1st Latin American Conference of Women in Bioinformatics and Data Science

C. Marino-Buslje, G. Stegmayer, Ivana Feldfeber, 2021, The Biophysicist.

Knowledge Component of a Multiagent Distributed Decision Support System

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

exp2GO: Improving Prediction of Functions in the Gene Ontology With Expression Data

Diego H. Milone, G. Stegmayer, A. Arce, 2022, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

ANN-Agent for Distributed Knowledge Source Discovery

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

Predicting novel microRNA: a comprehensive comparison of machine learning approaches

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

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.

Anc2vec: embedding gene ontology terms by preserving ancestors relationships

Diego H. Milone, G. Stegmayer, A. Edera, 2022, Briefings Bioinform..

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.

Automatic recognition of quarantine citrus diseases

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

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

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

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

Diego H. Milone, G. Stegmayer, M. Rubiolo, 2013, Neural Computing and Applications.

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

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

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

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

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).

Neural Networks and Volterra series for modeling new wireless communication devices

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

microRNA analysis of Taenia crassiceps cysticerci under praziquantel treatment and genome-wide identification of Taenia solium miRNAs.

Diego H. Milone, S. Asurmendi, G. Lichtenstein, 2017, International journal for parasitology.

miRe2e: a full end-to-end deep model based on transformers for prediction of pre-miRNAs

Diego H. Milone, J. Raad, L. Bugnon, 2021, Bioinform..

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

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

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.

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

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

Particle Size Distribution from Combined Light Scattering Measurements. A Neural Network Approach for Solving the Inverse Problem

G.S. Stegmayer, O.A. Chiotti, L.M. Gugliotta, 2006, 2006 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications.

High precision in microRNA prediction: A novel genome-wide approach with convolutional deep residual networks

Diego H. Milone, G Stegmayer, C Yones, 2021, Comput. Biol. Medicine.

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..

Genome-wide pre-miRNA discovery from few labeled examples

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

High precision in microRNA prediction: a novel genome-wide approach based on convolutional deep residual networks

Diego H. Milone, G. Stegmayer, C. Yones, 2020, bioRxiv.

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 .

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

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

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.

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

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

miRNAfe: A comprehensive tool for feature extraction in microRNA prediction

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

Metabolic pathways synthesis based on ant colony optimization

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

A new index for clustering validation with overlapped clusters

Diego H. Milone, D. N. Campo, G. Stegmayer, 2016, Expert Syst. Appl..

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

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

miRNAss: a semi-supervised approach for microRNA prediction

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

gGN: learning to represent graph nodes as low-rank Gaussian distributions

Diego H. Milone, G. Stegmayer, A. A. Edera, 2022, bioRxiv.

An evolutionary approach for searching metabolic pathways

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

Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics

Diego H. Milone, L. Bugnon, C. Yones, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Diversity control for improving the analysis of consensus clustering

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

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..

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

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

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).

Transfer learning: The key to functionally annotate the protein universe

Diego H. Milone, J. Raad, L. Bugnon, 2023, Patterns.

Evaluating large language models for annotating proteins

Diego H. Milone, L. Bugnon, G. Stegmayer, 2024, Briefings in bioinformatics.