Breast Cancer Biomarker Discovery Using Metabolomics and Machine Learning
Author Information
Author(s): Yagin Fatma Hilal, Gormez Yasin, Al-Hashem Fahaid, Ahmad Irshad, Ahmad Fuzail, Ardigò Luca Paolo
Primary Institution: Inonu University, Türkiye
Hypothesis
This study aims to identify targeted metabolomic biomarker candidates for the specific detection of breast cancer using explainable artificial intelligence.
Conclusion
The study identifies potential biomarkers for early breast cancer diagnosis and demonstrates the effectiveness of targeted metabolomics combined with machine learning.
Supporting Evidence
- Machine learning models showed high precision, recall, and specificity for breast cancer classification.
- SHAP values identified key metabolites like leucine and isoleucine as important biomarkers.
- The study utilized a hybrid methodology combining machine learning and explainable AI for better interpretability.
Takeaway
Researchers found specific substances in the blood that can help detect breast cancer early, which could lead to better treatment options.
Methodology
The study used plasma samples from breast cancer patients and healthy controls, applying machine learning models and SHAP for feature selection.
Limitations
The study lacks an independent validation cohort, which limits the generalizability of the predictive models.
Participant Demographics
102 breast cancer patients and 99 healthy controls, matched by age.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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