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%2F00216224%3A14330%2F21%3A00126185" target="_blank" >RIV/00216224:14330/21:00126185 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26220/21:PU142331

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

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of BIBM 2021

ISBN

9781665401265

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

3075-3082

Název nakladatele

IEEE

Místo vydání

Houston, TX, USA

Místo konání akce

Houston, TX, USA

Datum konání akce

1. 1. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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