Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis.

Journal: GigaScience
PMID: 38206587

Abstract

BACKGROUND: Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.

Authors

  • Akshay Akshay
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
  • Mitali Katoch
    Institute of Neuropathology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany.
  • Navid Shekarchizadeh
    Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany.
  • Masoud Abedi
    Department of Medical Data Science, Leipzig University Medical Centre, 04107 Leipzig, Germany.
  • Ankush Sharma
    KG Jebsen Centre for B-cell Malignancies, Institute for Clinical Medicine, University of Oslo, 0318 Oslo, Norway.
  • Fiona C Burkhard
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
  • Rosalyn M Adam
    Urological Diseases Research Center, Boston Children's Hospital, 02115 Boston, MA, USA.
  • Katia Monastyrskaya
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.
  • Ali Hashemi Gheinani
    Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.

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