## 3 Results
[21] constructed a dysregulated miRNA target network (DMTN) using miRNA-mRNA dyads that displayed significant dysregulation scores, indicating dysregulated nonsupervisory connections between miRNAs and target genes. The mRNA and miRNA expression biographies used in the study were attained from the genomic data commons (GDC1) data gate. All statistical and graphical analyses were conducted in the R terrain. The study linked 588 miRNAs and 3,146 genes associated with medicines, where the expression of miRNA genes showed significant associations with the response of cancer cells to anticancer medicines. The findings suggest that the expression situations of specific threat miRNAs and their neighboring monophasic genes could serve as pointers of cancer cell perceptivity to anticancer medicines. The experimenters proposed that with farther experimental and clinical confirmation, these miRNAs could potentially be used as biomarkers to guide the treatment of breast cancer cases. [PERSON] et al. [22] conducted a study using NGS data from breast cancer to identify the most important miRNA biomarkers. Selected miRNA biomarkers are strongly associated with multiple breast cancer subtypes. To do this, they used data from The Cancer Genome Atlas (TCGA) and proposed a two-step technique called feature selection methods embedded in machine learning, followed by survival analysis. In the first phase, to obtain this list of miRNAs, they selected the best among seven machine learning techniques (machine (SVM), artificial neural network (ANN), K-nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB) and Discriminant Analysis (DISCR)), using the entire set of features; the best machine learning technique in this RF case was selected. In the second phase, based on the classification accuracy values, the most important features from each selection method are considered to make a set that provides miRNAs such as 8 deg, 7 deg, and even 1 deg. These analytical results confirmed the fact that the selected miRNAs are potential biomarkers for the diagnosis of cancer subtypes. Furthermore, GO (gene ontology) enrichment analysis also revealed selected miRNA biological, molecular, and cellular processes related to breast cancer. Overall, this study identified all 27 miRNAs as potential biomarkers and found that they are responsible for different subtypes of breast cancer. [PERSON] et al. [23] proposed a new approach to use miRNA fractions as implicit biomarkers for the discovery of breast cancer. Their approach comprised two distinct stages. In the first stage, they employed two machine literacy models: a Support Vector Machine (SVM) to separate between healthy samples and cancer cells, and Random Forest (RF) to classify different subtypes of cancer. Through a comprehensive evaluation process using four-way cross-validation and grid hunt, they linked the most accurate model for each step, achieving a perfection of 0.9926 for the SVM and 0.9886 for the RF, along with the corresponding set of optimized hyperparameters. In the alternate stage, they employed a significance-grounded point selection approach to determine the crucial features employed by the machine literacy models to make their prognostications. This two-step approach involved using devoted classifiers for tumor/health classification and subtype discovery. A notable advantage of their study was the application of two entirely independent datasets for training and testing. These datasets were generated from different sequencing machines and passed distinct bioinformatics processes for data preprocessing. Their results in the bracket of healthy and tumorous samples yielded perfect situations, in line with the best-published findings. In particular, none of the tumor samples were misclassified as healthy, pressing the robustness of their approach.