A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Author(s)
Jana Schwarzerová, Ales Kostoval, Adam Bajger, Lucia Jakubikova, Iro Pierides, Lubos Popelinsky, Karel Sedlar, Wolfram Weckwerth
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.

Organisation(s)
Functional and Evolutionary Ecology, Research Platform Vienna Metabolomics Center
External organisation(s)
Brno University of Technology, Masaryk University, Universität Wien
Pages
498-509
DOI
https://doi.org/10.1007/978-3-031-09135-3_42
Publication date
06-2022
Peer reviewed
Yes
Austrian Fields of Science 2012
106057 Metabolomics, 101028 Mathematical modelling, 102019 Machine learning
Keywords
Portal url
https://ucris.univie.ac.at/portal/en/publications/a-revealed-imperfection-in-concept-drift-correction-in-metabolomics-modeling(6c60e7c5-3bbf-44a5-96db-d94c6765504a).html