Detailed Information on Publication Record
2021
An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
SCHWARZEROVÁ, Jana, Adam BAJGER, I. PIERDOU, Lubomír POPELÍNSKÝ, Karel SEDLÁŘ et. al.Basic information
Original name
An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
Name in Czech
An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
Authors
SCHWARZEROVÁ, Jana (203 Czech Republic), Adam BAJGER (203 Czech Republic, belonging to the institution), I. PIERDOU, Lubomír POPELÍNSKÝ (203 Czech Republic, belonging to the institution), Karel SEDLÁŘ (203 Czech Republic) and W. WECKWERTH
Edition
Houston, TX, USA, Proceedings of BIBM 2021, p. 3075-3082, 8 pp. 2021
Publisher
IEEE
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10602 Biology , Evolutionary biology
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
RIV identification code
RIV/00216224:14330/21:00126185
Organization unit
Faculty of Informatics
ISBN
978-1-6654-0126-5
Keywords (in Czech)
Machine Learning; Concept drift; Metabolomics Analysis; Biomedical engineering; Computational biomedical analysis; Metabolomic prediction
Keywords in English
Machine Learning; Concept drift; Metabolomics Analysis; Biomedical engineering; Computational biomedical analysis; Metabolomic prediction
Tags
International impact, Reviewed
Změněno: 28/3/2023 11:24, RNDr. Pavel Šmerk, Ph.D.
V originále
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.
In Czech
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.