An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU142331" target="_blank" >RIV/00216305:26220/21:PU142331 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14330/21:00126185
Result on the web
<a href="http://dx.doi.org/10.1109/BIBM52615.2021.9669418" target="_blank" >http://dx.doi.org/10.1109/BIBM52615.2021.9669418</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM52615.2021.9669418" target="_blank" >10.1109/BIBM52615.2021.9669418</a>
Alternative languages
Result language
angličtina
Original language name
An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection
Original language description
One of the most challenging scenarios of data analysis is prediction using time series data. As the underlying causal relationships of the data shift over time, a classification model trained on data at earlier points within the course starts to yield incorrect predictions on the current data. This phenomenon in machine learning is called concept drift. Within biomedical data, one of the molecular networks that changes significantly over a time is the metabolome. Using metabolomics analysis in biomedical applications produces an ideal tool in preventive healthcare, the pharmaceutical industry, and even ecology engineering. This study provides an innovative perspective on the analysis of metabolomics datasets using the concept of drift detection. The evaluation is based on two main objectives. The first objective is connected to the concept drift detection in available metabolomics datasets, and the second objective is to provide the assessment of commonly used machine learning tools for the best general detection approach in metabolomics datasets. The application of concept drift to metabolomics data has never been carried out before and is an original take on the analysis of highly dynamic molecular networks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30403 - Technologies involving identifying the functioning of DNA, proteins and enzymes and how they influence the onset of disease and maintenance of well-being (gene-based diagnostics and therapeutic interventions [pharmacogenomics, gene-based therapeutics])
Result continuities
Project
<a href="/en/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Quality internal grants at BUT</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN
978-1-6654-0126-5
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
3075-3082
Publisher name
Neuveden
Place of publication
neuveden
Event location
Houston, Texas
Event date
Dec 9, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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