Georgina Stegmayer
Georgina Stegmayer
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Cited by
Cited by
A neural network model for estimating the particle size distribution of dilute latex from multiangle dynamic light scattering measurements
LM Gugliotta, GS Stegmayer, LA Clementi, VDG Gonzalez, RJ Minari, ...
Particle & Particle Systems Characterization 26 (1‐2), 41-52, 2009
Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model
M Rubiolo, ML Caliusco, G Stegmayer, M Coronel, MG Fabrizi
Information Sciences 194, 107-119, 2012
* omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants
DH Milone, GS Stegmayer, L Kamenetzky, M López, JM Lee, ...
BMC bioinformatics 11 (1), 438, 2010
miRNAfe: a comprehensive tool for feature extraction in microRNA prediction
CA Yones, G Stegmayer, L Kamenetzky, DH Milone
Biosystems 138, 1-5, 2015
Automatic recognition of quarantine citrus diseases
G Stegmayer, DH Milone, S Garran, L Burdyn
Expert systems with applications 40 (9), 3512-3517, 2013
Volterra series and neural networks to model an electronic device nonlinear behavior
G Stegmayer
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No …, 2004
Towards a Volterra series representation from a Neural Network model
G Stegmayer, M Pirola, G Orengo, O Chiotti
WSEAS Transactions on Systems 3 (2), 432-437, 2004
A new index for clustering validation with overlapped clusters
DN Campo, G Stegmayer, DH Milone
Expert Systems with Applications 64, 549-556, 2016
Semantic web technologies and artificial neural networks for intelligent web knowledge source discovery
ML Caliusco, G Stegmayer
Emergent web intelligence: Advanced semantic technologies, 17-36, 2010
High class-imbalance in pre-miRNA prediction: a novel approach based on deepSOM
G Stegmayer, C Yones, L Kamenetzky, DH Milone
IEEE/ACM transactions on computational biology and bioinformatics 14 (6 …, 2016
Data mining over biological datasets: An integrated approach based on computational intelligence
G Stegmayer, M Gerard, DH Milone
IEEE Computational Intelligence Magazine 7 (4), 22-34, 2012
A biologically inspired validity measure for comparison of clustering methods over metabolic data sets
G Stegmayer, DH Milone, L Kamenetzky, MG Lopez, F Carrari
IEEE/ACM transactions on computational biology and bioinformatics 9 (3), 706-716, 2012
Neural network model for integration and visualization of introgressed genome and metabolite data
G Stegmayer, D Milone, L Kamenetzky, M Lopez, F Carrari
2009 International Joint Conference on Neural Networks, 2983-2989, 2009
Metabolic analyses of interspecific tomato recombinant inbred lines for fruit quality improvement
MG López, MI Zanor, GR Pratta, G Stegmayer, SB Boggio, M Conte, ...
Metabolomics 11 (5), 1416-1431, 2015
Volterra NN-based behavioral model for new wireless communications devices
G Stegmayer, O Chiotti
Neural Computing and Applications 18 (3), 283-291, 2009
Industrial SBR process: Computer simulation study for online estimation of steady‐state variables using neural networks
RJ Minari, GS Stegmayer, LM Gugliotta, OA Chiotti, JR Vega
Macromolecular Reaction Engineering 1 (3), 405-412, 2007
Predicting novel microRNA: a comprehensive comparison of machine learning approaches
G Stegmayer, LE Di Persia, M Rubiolo, M Gerard, M Pividori, C Yones, ...
Briefings in bioinformatics 20 (5), 1607-1620, 2019
Extreme learning machines for reverse engineering of gene regulatory networks from expression time series
M Rubiolo, DH Milone, G Stegmayer
Bioinformatics 34 (7), 1253-1260, 2018
A very simple and fast way to access and validate algorithms in reproducible research
G Stegmayer, M Pividori, DH Milone
Briefings in bioinformatics 17 (1), 180-183, 2016
Clustering biological data with SOMs: On topology preservation in non-linear dimensional reduction
DH Milone, G Stegmayer, L Kamenetzky, M LóPez, F Carrari
Expert systems with applications 40 (9), 3841-3845, 2013
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