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. |
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@inproceedings{1863732, author = {Schwarzerová, Jana and Kostoval, Aleš and Bajger, Adam and Jakubíková, Lucia and Pierdou, Iro and Popelínský, Lubomír and Sedlář, K. and Weckwerth, Wolfram}, address = {Kamien Slaski}, booktitle = {Information Technology in Biomedicine: 9th International Conference, ITIB 2022}, doi = {http://dx.doi.org/10.1007/978-3-031-09135-3_42}, editor = {Ewa Pietka, Pawel Badura, Jacek Kawa, Wojciech Wieclawek}, keywords = {Biomedical analysis; Metabolomics; Machine learning; Prediction methods}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Kamien Slaski}, isbn = {978-3-031-09134-6}, pages = {498-509}, publisher = {Springer}, title = {A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling}, year = {2022} }
TY - JOUR ID - 1863732 AU - Schwarzerová, Jana - Kostoval, Aleš - Bajger, Adam - Jakubíková, Lucia - Pierdou, Iro - Popelínský, Lubomír - Sedlář, K. - Weckwerth, Wolfram PY - 2022 TI - A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling PB - Springer CY - Kamien Slaski SN - 9783031091346 KW - Biomedical analysis KW - Metabolomics KW - Machine learning KW - Prediction methods N2 - 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. ER -
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. \textit{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.
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