Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F25%3AE0111874" target="_blank" >RIV/00843989:_____/25:E0111874 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/25:00143831

Result on the web

<a href="https://doi.org/10.1093/bioadv/vbaf073" target="_blank" >https://doi.org/10.1093/bioadv/vbaf073</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1093/bioadv/vbaf073" target="_blank" >10.1093/bioadv/vbaf073</a>

Result language

angličtina

Original language name

Enhanced metabolomic predictions using concept drift analysis: identification and correction of confounding factors

Original language description

Motivation: The increasing use of big data and optimized prediction methods in metabolomics requires techniques aligned with biological assumptions to improve early symptom diagnosis. One major challenge in predictive data analysis is handling confounding factors-variables influencing predictions but not directly included in the analysis. Results: Detecting and correcting confounding factors enhances prediction accuracy, reducing false negatives that contribute to diagnostic errors. This study reviews concept drift detection methods in metabolomic predictions and selects the most appropriate ones. We introduce a new implementation of concept drift analysis in predictive classifiers using metabolomics data. Known confounding factors were confirmed, validating our approach and aligning it with conventional methods. Additionally, we identified potential confounding factors that may influence biomarker analysis, which could introduce bias and impact model performance. Availability and implementation: Based on biological assumptions supported by detected concept drift, these confounding factors were incorporated into correction of prediction algorithms to enhance their accuracy. The proposed methodology has been implemented in Semi-Automated Pipeline using Concept Drift Analysis for improving Metabolomic Predictions (SAPCDAMP), an open-source workflow available at https://github.com/JanaSchwarzerova/SAPCDAMP.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Bioinformatics advances

ISSN

2635-0041

e-ISSN

2635-0041

Volume of the periodical

5

Issue of the periodical within the volume

article vbaf073

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

1-12

UT code for WoS article

001477347000001

EID of the result in the Scopus database

2-s2.0-105003944866