Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors
Based on biological assumptions supported by detected concept drift, these confounding factors were incorporated into correction of prediction algorithms to enhance their accuracy. The proposed methodology has been implemented in Semi-Automated Pipeline using Concept Drift Analysis for improving Metabolomic Predictions (SAPCDAMP), an open-source workflow available at https://github.com/JanaSchwarzerova/SAPCDAMP.
Authors
- David Friedecký (ORCID: https://orcid.org/0000-0002-3448-9073)
- Jana Schwarzerová (ORCID: https://orcid.org/0000-0003-2918-9313)
- Jitka Poměnková (ORCID: https://orcid.org/0000-0002-8060-0086)
- Katerina Jureckova (ORCID: https://orcid.org/0000-0001-5143-9521)
- Jiří Sekora
- Wolfram Weckwerth (ORCID: https://orcid.org/0000-0002-9719-6358)
- Aleš Kvasnička (ORCID: https://orcid.org/0000-0001-5973-316X)
- Dominika Olešová (ORCID: https://orcid.org/0000-0001-6840-5248)
- Luboš Popeĺınský
- Valentine Provaznik
- Ales Kostoval
Institutions
- University of Vienna (AT)
- Institute of Chemistry of the Slovak Academy of Sciences (SK)
- Slovak Academy of Sciences (SK)
- Masaryk University (CZ)
- University Hospital Ostrava (CZ)
- Institute of Experimental Endocrinology of the Slovak Academy of Sciences (SK)
- University Hospital Olomouc (CZ)
- Institute of Neuroimmunology of the Slovak Academy of Sciences (SK)
- Brno University of Technology (CZ)
- Palacký University Olomouc (CZ)
- Medical University of Vienna (AT)
Publication Details
- Journal
- Bioinformatics Advances
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/bioadv/vbaf073
- Citations
- 2
- Primary Topic
- Data Stream Mining Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- Masarykova Univerzita