Georgina Stegmayer

  • Independent Researcher, CONICET
  • Assistant Professor, Department of Informatics, FICH-UNL
  • Leader of the Bioinformatics Research Group: computer science and machine learning people who essentially make algorithms for the integration, fusion and analysis of biological data. We develop novel data mining algorithms and computing tools, as well as intelligent algorithms, for biological data fusion and integration, and for new knowledge discovery.

Research lines in bioinformatics:

  • Gene function prediction – inference of GO annotations to genes
  • Gene regulatory network reconstruction
  • Metabolic engineering
  • Pre-miRNA prediction algorithms from genome-wide data
  • Data fusion and integration of biological sources

Research Interests

  • Bioinformatics
  • Machine learning
  • Neural networks
  • Data mining
  • Clustering

Teaching

Courses in Engineering Informatics:

Advisor of

Coadvisor of

Latest Publicactions

DL4papers: a deep learning approach for the automatic interpretation of scientific articles
  • L. A. Bugnon
  • C. Yones
  • J. Raad
  • M. Gerard
  • M. Rubiolo
  • G. Merino
  • M. Pividori
  • L. Di Persia
  • D. H. Milone
  • G. Stegmayer

Oxford Bioinformatics - 2020

Complexity measures of the mature miRNA for improving pre-miRNAs prediction
  • J. Raad
  • G. Stegmayer
  • D. H. Milone

Bioinformatics - 2020

Metabolic pathways synthesis based on ant colony optimization
  • M. Gerard
  • G. Stegmayer
  • D. H. Milone

XX Simposio Argentino de Inteligencia Artificial (ASAI). 48 Jornadas Argentinas de Informática (JAIIO) - UNSa - 2019

Deep neural architectures for highly imbalanced data in bioinformatics
  • L. A. Bugnon
  • C. Yones
  • D. H. Milone
  • G. Stegmayer

Argentine Symposium on Data Science and Big Data, AGRANDA 2019, 48º JAIIO - 2019

MicroRNA prediction from genome-wide data with deep learning: a novel approach based on convolutional residual networks
  • C. Yones
  • L. A. Bugnon
  • J. Raad
  • D. H. Milone
  • G. Stegmayer

A2B2C 10th Meeting - 2019

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