## 14 Conclusions  
This work proposed a pipeline to integrate multiomics data to classify the menopause status in female breast cancer. The aim of the study was to identify multiomics biomarkers  
Figure 8: Boxplot for the gene expression values for CDH1 (\(p\) value: \(2.16\times 10^{-2}\)), MAP3K1 (\(p\) value: \(4.51\times 10^{-1}\)), PTEN (\(p\) value: \(1.2\times 10^{-5}\)), and RUNX1 (\(p\) value: \(4.03\times 10^{-9}\)) in both premenopause and postmenopausal samples.  
for premenopausal and postmenopausal to understand how this physical and emotional change is reflected in the molecular base in breast cancer tissue. The dataset contains gene expression, CNA, and DNA methylation data that are processed and merged into a classification model based on 10 crossvalidations. SVM-RBF, Random Forest, and Naive Bayes were tested in the merged data.  
Recognizing the imbalance in the distribution of samples between premenopausal and postmenopausal classes, we addressed this issue using the Synthetic Minority Oversampling Technique (SMOTE). This up-sampling method generated synthetic samples within the minority class, thereby providing a more balanced dataset for subsequent analyses.  
The Random Forest exhibited the highest performance with an AUCROC of 0.962, while the SVM-RBF demonstrated superior accuracy, thereby achieving 89.53%. Pathway analysis on the selected genes revealed an association between those genes and some cancer pathways; one of them is the \"ErbB signaling pathway\", which was reported to be associated with menopausal syndrome and cardiorenal disease. The survival analysis for both classes, premenopausal and postmenopausal in breast cancer, showed a significant difference in the survival rate between them, which makes them two distinct populations, with each having its own characteristics. Explainable AI Shapley based on XGBoost showed that RUNX1, PTEN, MAP3K1, and CDH1 had the highest impact in distinguishing the two classes. The literature confirmed the associations between each one of them with breast cancer in general and menopause status.  
The source code is available on the following GitHub repository: [[https://github.com/dtabed/PCAOMICS](https://github.com/dtabed/PCAOMICS)]([https://github.com/dtabed/PCAOMICS](https://github.com/dtabed/PCAOMICS)) (accessed on 12 December 2023).  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/a17010013/s1](https://www.mdpi.com/article/10.3390/a17010013/s1)]([https://www.mdpi.com/article/10.3390/a17010013/s1](https://www.mdpi.com/article/10.3390/a17010013/s1)). Figure S1: Pearson--Correlation matrix for the gene expression omic dataset that shows only the correlation value for the significant \(p\) value \(<\) 0.05; Figure S2: Pearson--Correlation matrix for the CNA omic dataset that shows only the correlation value for the significant \(p\) value \(<\) 0.05; Figure S3: Pearson--Correlation matrix for the DNA Methylation omic dataset that shows only the correlation value for the significant \(p\) value \(<\) 0.05.  
**Author Contributions:** Conceptualization, A.A., O.B. and H.Q.; data curation, F.A. and A.A.-R.; formal analysis, F.A., I.A.-H., A.A. and H.Q.; funding acquisition, H.Q., A.A., O.B. and A.A.-R.; investigation, F.A., A.A., O.B., H.Q. and A.A.-R.; methodology, F.A., I.A.-H. and A.A.; project administration, A.A. and S.I.; resources, A.A., O.B., H.Q. and A.A.-R. Supervision; S.I. All authors participated in the writing and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work received funding from the Scientific Research and Innovation Support Fund/ Ministry of Higher Education and the Scientific Research/Jordan grant number (ICT/1/16/2022).  
**Data Availability Statement:** The processed dataset is available in the Supplementary Materials.  
**Conflicts of Interest:** The authors declare no conflicts of interest.