Detail publikačního výsledku

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

SCHWARZEROVÁ, J.; KOŠTOVAL, A.; BAJGER, A.; JAKUBIKOVA, L.; PIERDIES, I.; POPELINSKY, L.; SEDLÁŘ, K.; WECKWERTH, W.

Originální název

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Anglický název

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Biomedical analysis, Metabolomics, Machine learning, Prediction methods

Klíčová slova v angličtině

Biomedical analysis, Metabolomics, Machine learning, Prediction methods

Autoři

SCHWARZEROVÁ, J.; KOŠTOVAL, A.; BAJGER, A.; JAKUBIKOVA, L.; PIERDIES, I.; POPELINSKY, L.; SEDLÁŘ, K.; WECKWERTH, W.

Rok RIV

2023

Vydáno

23.06.2022

Nakladatel

Springer

ISBN

978-3-031-09135-3

Kniha

Information Technology in Biomedicine

Strany od

498

Strany do

509

Strany počet

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"
}