Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence

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dc.authoridYAGIN, Fatma Hilal/0000-0002-9848-7958
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGormez, Yasin
dc.contributor.authorAl-Hashem, Fahaid
dc.contributor.authorAhmad, Irshad
dc.contributor.authorAhmad, Fuzail
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2025-05-04T16:45:49Z
dc.date.available2025-05-04T16:45:49Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractBackground Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.Methods Data obtained after targeted metabolomics analyses using plasma samples from BC patients (n = 102) and healthy controls (n = 99) were used. Machine learning (ML) models based on raw data were developed, then feature selection methods were applied, and the results were compared. SHapley Additive exPlanations (SHAP), an XAI method, was used to clinically explain the decisions of the optimal model in BC prediction.Results The results revealed that variable selection increased the performance of ML models in BC classification, and the optimal model was obtained with the logistic regression (LR) classifier after support vector machine (SVM)-SHAP-based feature selection. SHAP annotations of the LR model revealed that Leucine, isoleucine, L-alloisoleucine, norleucine, and homoserine acids were the most important potential BC diagnostic biomarkers. Combining the identified metabolite markers provided robust BC classification measures with precision, recall, and specificity of 89.50%, 88.38%, and 83.67%, respectively.Conclusion In conclusion, this study adds valuable information to the discovery of BC biomarkers and underscores the potential of targeted metabolomics-based diagnostic advances in the management of BC.
dc.description.sponsorshipDeanship of Research and Graduate Studies at King Khalid University [RGP-2/202/45]
dc.description.sponsorshipFA would like to thank Almaarefa University for supporting this research.
dc.identifier.doi10.3389/fmolb.2024.1426964
dc.identifier.issn2296-889X
dc.identifier.pmid39744676
dc.identifier.scopus2-s2.0-85213686865
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3389/fmolb.2024.1426964
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35221
dc.identifier.volume11
dc.identifier.wosWOS:001386254400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.ispartofFrontiers in Molecular Biosciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250504
dc.subjectbreast cancer
dc.subjectmetabolomics
dc.subjectfeature selection
dc.subjectexplainable artificial intelligence
dc.subjectprognostic model
dc.titleBiomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explainable artificial intelligence
dc.typeArticle

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