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All Journal Eksakta : Berkala Ilmiah Bidang MIPA
Ratnaningayu, Nindhyana Diwaratri
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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The Implementation of Machine Learning Algorithms for Breast Cancer Biomarker Validation in Metabolomics Studies Ratnaningayu, Nindhyana Diwaratri; Tedjo, Aryo; Sonar Soni Panigoro
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 04 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss04/553

Abstract

Breast cancer is a heterogeneous disease characterized by distinct molecular and metabolic characteristics, making its diagnostics and treatment challenging. The existence of metabolic reprogramming in breast cancer underscores the potential to identify biomarkers through metabolomics studies, offering new avenues for personalized therapeutic approaches. Machine learning algorithms are now increasingly used to uncover complex patterns in metabolomics data. A comprehensive analysis of in silico metabolomics had successfully identified 24 significant metabolites after rigorous univariate and multivariate tests. Pathway analysis highlighted the apparent involvement of glycerolphosphate in glycerophospholipid and glycerolipid metabolism, indicating its potential role in breast cancer pathology. Validation of these 24 metabolites using machine learning algorithms provided superior results, with Neural Network achieving an AUC of 0.979 and a precision of 93%, Logistic Regression showing an AUC of 0.945 and a precision of 95.7%, as well as Random Forest reporting an AUC of 0.974 and a precision of 95.7% in predictive performance. These findings demonstrate the remarkable ability of machine learning to improve biomarker validation accuracy in metabolomics, facilitating better diagnostic strategies for breast cancer.
Co-Authors Aryo Tedjo Sonar Soni Panigoro