A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00126187" target="_blank" >RIV/00216224:14330/22:00126187 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-09135-3_42" target="_blank" >http://dx.doi.org/10.1007/978-3-031-09135-3_42</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-09135-3_42" target="_blank" >10.1007/978-3-031-09135-3_42</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Popis výsledku v původním jazyce

Prediction models that rely on time series data are often affected by diminished predictive accuracy. This occurs from the causal relationships of the data that shift over time. Thus, the changing weights that are used to create prediction models lose their informational value. One way to correct this change is by using concept drift information. That is exactly what prediction models in biomedical applications need. Currently, metabolomics is at the forefront in modeling analysis for phenotype prediction, making it one of the most interesting candidates for biomedical prediction diagnosis. However, metabolomics datasets include dynamic information that can harm prediction modeling. This study presents a concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.

Název v anglickém jazyce

A Revealed Imperfection in Concept Drift Correction in Metabolomics Modeling

Popis výsledku anglicky

Prediction models that rely on time series data are often affected by diminished predictive accuracy. This occurs from the causal relationships of the data that shift over time. Thus, the changing weights that are used to create prediction models lose their informational value. One way to correct this change is by using concept drift information. That is exactly what prediction models in biomedical applications need. Currently, metabolomics is at the forefront in modeling analysis for phenotype prediction, making it one of the most interesting candidates for biomedical prediction diagnosis. However, metabolomics datasets include dynamic information that can harm prediction modeling. This study presents a concept drift correction methods to account for dynamic changes that occur in metabolomics data for better prediction outcomes of phenotypes in a biomedical setting.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Information Technology in Biomedicine

ISBN

9783031091346

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

12

Strana od-do

498-509

Název nakladatele

Information Technology in Biomedicine: 9th International Conference, ITIB 2022

Místo vydání

Kamien Slaski

Místo konání akce

Kamien Slaski

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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