A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

SCHWARZEROVÁ, Jana, Aleš KOSTOVAL, Adam BAJGER, Lucia JAKUBÍKOVÁ, Iro PIERDOU, Lubomír POPELÍNSKÝ, K. SEDLÁŘ and 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, p. 498-509. ISBN 978-3-031-09134-6. Available from: https://dx.doi.org/10.1007/978-3-031-09135-3_42.

Basic information

Original name

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Name in Czech

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Authors

SCHWARZEROVÁ, Jana (203 Czech Republic), Aleš KOSTOVAL, Adam BAJGER (203 Czech Republic, belonging to the institution), Lucia JAKUBÍKOVÁ (703 Slovakia, belonging to the institution), Iro PIERDOU, Lubomír POPELÍNSKÝ (203 Czech Republic, belonging to the institution), K. SEDLÁŘ and Wolfram WECKWERTH

Edition

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

Publisher

Springer

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Poland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

RIV identification code

RIV/00216224:14330/22:00126187

Organization unit

Faculty of Informatics

ISBN

978-3-031-09134-6

ISSN

DOI

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

Keywords (in Czech)

Biomedical analysis; Metabolomics; Machine learning; Prediction methods

Keywords in English

Biomedical analysis; Metabolomics; Machine learning; Prediction methods

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Abstract

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.

In Czech

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.