An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216224:14330/21:00126185

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

An Innovative Perspective on Metabolomics Data Analysis in Biomedical Research Using Concept Drift Detection

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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])

Projekt

<a href="/cs/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Kvalitní interní granty VUT</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ISBN

978-1-6654-0126-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

3075-3082

Název nakladatele

Neuveden

Místo vydání

neuveden

Místo konání akce

Houston, Texas

Datum konání akce

9. 12. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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