A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

SCHWARZEROVÁ, Jana, Aleš KOSTOVAL, Adam BAJGER, Lucia JAKUBÍKOVÁ, Iro PIERDOU, Lubomír POPELÍNSKÝ, K. SEDLÁŘ a Wolfram WECKWERTH. A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling. In Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek. Information Technology in Biomedicine: 9th International Conference, ITIB 2022. Kamien Slaski: Springer, 2022, s. 498-509. ISBN 978-3-031-09134-6. Dostupné z: https://dx.doi.org/10.1007/978-3-031-09135-3_42.

Základní údaje

Originální název

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Název česky

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Autoři

SCHWARZEROVÁ, Jana (203 Česká republika), Aleš KOSTOVAL, Adam BAJGER (203 Česká republika, domácí), Lucia JAKUBÍKOVÁ (703 Slovensko, domácí), Iro PIERDOU, Lubomír POPELÍNSKÝ (203 Česká republika, domácí), K. SEDLÁŘ a Wolfram WECKWERTH

Vydání

Kamien Slaski, Information Technology in Biomedicine: 9th International Conference, ITIB 2022, od s. 498-509, 12 s. 2022

Nakladatel

Springer

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Polsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/22:00126187

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-09134-6

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-09135-3_42

Klíčová slova česky

Biomedical analysis; Metabolomics; Machine learning; Prediction methods

Klíčová slova anglicky

Biomedical analysis; Metabolomics; Machine learning; Prediction methods

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Anotace

V originále

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. This study presents a concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.

Česky

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. This study presents a concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.