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@inproceedings{1863712,
author = {Schwarzerová, Jana and Bajger, Adam and PIERDOU, I. and Popelínský, Lubomír and Sedlář, Karel and WECKWERTH, W.},
address = {Houston, TX, USA},
booktitle = {Proceedings of BIBM 2021},
doi = {http://dx.doi.org/10.1109/BIBM52615.2021.9669418},
editor = {Yufei Huang and Lukasz A. Kurgan and Feng Luo and Xiaohua Hu and Yidong Chen and Edward R. Dougherty and Andrzej Kloczkowski and Yaohang Li},
keywords = {Machine Learning; Concept drift; Metabolomics Analysis; Biomedical engineering; Computational biomedical analysis; Metabolomic prediction},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Houston, TX, USA},
isbn = {978-1-6654-0126-5},
pages = {3075-3082},
publisher = {IEEE},
title = {An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection},
year = {2021}
}
TY - JOUR
ID - 1863712
AU - Schwarzerová, Jana - Bajger, Adam - PIERDOU, I. - Popelínský, Lubomír - Sedlář, Karel - WECKWERTH, W.
PY - 2021
TI - An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
PB - IEEE
CY - Houston, TX, USA
SN - 9781665401265
KW - Machine Learning
KW - Concept drift
KW - Metabolomics Analysis
KW - Biomedical engineering
KW - Computational biomedical analysis
KW - Metabolomic prediction
N2 - The most challenging applications of data analysis prediction are mostly related to scenarios, where the source data is being provided in a time course. As the distribution of the underlying reality shifts over a time, a classification model trained on the previously relevant data starts to yield incorrect predictions about the data that are relevant right now. This phenomenon in machine learning is called concept drift. Within biomedical data, one of the molecular networks that is most significantly changing over a time, is the metabolome. Using metabolomics analysis to biomedical applications, makes an ideal tool for preventive healthcare, pharmaceutical industry, and even ecology engineering. This study provides an innovated perspective on the analysis of metabolomics datasets using the concept of drift detection. The evaluation is based on two main goals. The first goal is connected to the concept drift detection in available metabolomics datasets and the second goal is to provide the assessment of commonly used tools, resulting in the best detection approach for a general metabolomics dataset.
ER -
SCHWARZEROVÁ, Jana, Adam BAJGER, I. PIERDOU, Lubomír POPELÍNSKÝ, Karel SEDLÁŘ and W. WECKWERTH. An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection. Online. In Yufei Huang and Lukasz A. Kurgan and Feng Luo and Xiaohua Hu and Yidong Chen and Edward R. Dougherty and Andrzej Kloczkowski and Yaohang Li. \textit{Proceedings of BIBM 2021}. Houston, TX, USA: IEEE, 2021, p.~3075-3082. ISBN~978-1-6654-0126-5. Available from: https://dx.doi.org/10.1109/BIBM52615.2021.9669418.
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