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SCHWARZEROVÁ, J. KOŠTOVAL, A. BAJGER, A. JAKUBIKOVA, L. PIERDIES, I. POPELINSKY, L. SEDLÁŘ, K. WECKWERTH, W.
Original Title
A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling
Type
conference paper
Language
English
Original Abstract
Prediction models that rely on time series data are often affected by diminished predictive accuracy. This occurs from the causal relationships of the data that shift over time. Thus, the changing weights that are used to create prediction models lose their informational value. One way to correct this change is by using concept drift information. That is exactly what prediction models in biomedical applications need. Currently, metabolomics is at the forefront in modeling analysis for phenotype prediction, making it one of the most interesting candidates for biomedical prediction diagnosis. However, metabolomics datasets include dynamic information that can harm prediction modeling. The study presents concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.
Keywords
Biomedical analysis, Metabolomics, Machine learning, Prediction methods
Authors
SCHWARZEROVÁ, J.; KOŠTOVAL, A.; BAJGER, A.; JAKUBIKOVA, L.; PIERDIES, I.; POPELINSKY, L.; SEDLÁŘ, K.; WECKWERTH, W.
Released
23. 6. 2022
Publisher
Springer
ISBN
978-3-031-09135-3
Book
Information Technology in Biomedicine
Pages from
498
Pages to
509
Pages count
12
BibTex
@inproceedings{BUT178419, author="Jana {Schwarzerová} and Aleš {Koštoval} and Adam {Bajger} and Lucia {Jakubikova} and Iro {Pierdies} and Lubos {Popelinsky} and Karel {Sedlář} and Wolfram {Weckwerth}", title="A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling", booktitle="Information Technology in Biomedicine", year="2022", pages="498--509", publisher="Springer", doi="10.1007/978-3-031-09135-3", isbn="978-3-031-09135-3" }