## 4 Discussion  
Several machine learning methods exist to identify potential miRNA targets in BRCA. With this review, we intend to analyze the studies of different methods according to the level of bioinformatics technique, and ensure their stability according to the characteristics used. Firstly, in all the analyzed studies, it was possible to assess the scope of the contribution of bioinformatics for the detection and treatment of breast cancer, and it was perceived over time that, together with other types of analyses (in vivo, in vitro), there is support in the fight against this disease. The study by [PERSON] et al. [18] coincides with the research of [PERSON] et al. [24] that addresses the topic of triple-negative breast cancer (TNBC). Regarding the role of fibroblasts, it also uses the Gene Expression Omnibus (GEO) dataset and Cancer Genome Atlas (TCGA) datasets: Random Forest (RF), Decision Tree (DT), and K-nearest neighbors (KNN) from the R package \"caret\", in contrast to the machine learning methods used, to create predictions for cancer-associated fibroblast (CAF) subtypes, resulting in an area under the curve of 0.921 with an RF model integrated into the experimental dataset. Significant genes and their differential expression were obtained with _p_-value \(\leq\) 0.05 and fold change > 0.5, which was different from that applied by [PERSON] et al. [18], whereby it was \(\geq\)1. On the other hand, research by [PERSON] et al. [19] does not agree with [PERSON] et al. [25], as they focused on the robustness and portability of PAM50. The closest central classifier was developed using microarray data to identify five \"intrinsic subtypes\" of breast cancer. They also proposed a strategy called \"average within class\" (AWCA) that improves classification power with more than 90 matches and predictive power. They agreed that they used data from the Cancer Genome Atlas (TCGA); however, they did not use _p_-value and fold-change parameters for significant gene expression. [PERSON] [20] used a tree-based and matching classifier [PERSON] et al. [26] that has a hierarchical machine learning system that assigns each node a relative class other, making the model tree-based. This study analyzed datasets available on the cBioPortal. Naive Bayes and Random Forest were used as classifiers. Finally, several biomarker genes are identified to predict the appropriate treatment for the patient. They obtained a 97.9% accuracy for the decision tree. For samples of live hormones based on the correlation coefficient between gene expressions, they used \(p\) < 0.05 that differs from that proposed by [PERSON] [20]. [PERSON] et al. [21] used a support vector machine classifier that coincides with the work of [PERSON] and [PERSON] [27] that proposes a Support Vector Machine (SVM)-based classifier to classify patients with early and advanced breast cancer. SVM-BRC uses an optimal feature selection method, a legacy bio-target combinatorial genetic algorithm to identify a miRNA signature, which is a small set of informative miRNAs. They also agree to process the breast cancer miRNA expression profile data from the Cancer Genome Atlas. They show a significant association with the prognosis of the breast cancer patient using the _p_-value and fold change at different significant intervals. Regarding the performance of SVM-BRC, they achieved an accuracy of 83.16%, which was the best compared to other classifiers. In relation to the work of [PERSON] et al. [22], some coincidences are observed with the study by [PERSON] et al. [28]. The first is that they used to validate seven well-known machine learning methods computationally: Logistic Regression (LR), Decision Tree (DT), Artificial Neural Network (RNA), also known as Multilayer Perceptron, Support Vector Machine with linear kernel (SVM), K-nearest neighbor (K-NN), Random Forest (RF), and finally Naive Bayes Classifier (NB). The second is that the expression and clinical data of miRNA-seq of invasive breast carcinoma (BRCA) were obtained from The Cancer Genome Atlas.