ENNGene: an Easy Neural Network model building tool for Genomics.

Journal: BMC genomics
Published Date: Mar 31, 2022

Abstract

BACKGROUND: The recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field.

Authors

  • Eliška Chalupová
    Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czechia.
  • Ondřej Vaculík
    Faculty of Science, National Centre for Biomolecular Research, Masaryk University, Brno, Czechia.
  • Jakub Poláček
    Faculty of Informatics, Masaryk University, Brno, Czechia.
  • Filip Jozefov
    Faculty of Informatics, Masaryk University, Brno, Czechia.
  • Tomas Majtner
    Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230, Odense, Denmark.
  • Panagiotis Alexiou
    Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia. panagiotis.alexiou@ceitec.muni.cz.

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