乳腺癌是一个主要影响女性的全球性问题，如果不及早发现，就会构成重大威胁。虽然乳腺癌患者的生存率通常较高，但区域转移的出现明显降低了生存前景。检测转移并理解其分子基础对于制定有效的治疗方法和改善患者的生存结果至关重要。本研究采用了各种人工智能方法和技术来获得准确的结果。最初，对数据进行组织并进行保留交叉验证、数据清理和标准化。随后，使用方差分析和二元粒子群优化（PSO）进行特征选择。在分析阶段，使用机器学习分类算法评估所选特征的判别力。最后，考虑所选择的特征，并利用SHAP算法来识别增强淋巴结转移主要分子机制解码的最显着特征。在本研究中，mRNA表达数据的分析遵循五个主要步骤：读取、预处理、特征选择、分类和SHAP算法。 RF 分类器利用候选 mRNA 区分阴性和阳性类别，准确度为 61%，AUC 为 0.6。在 SHAP 过程中，发现了所选 mRNA 与阳性/阴性淋巴结状态之间的有趣关系。结果表明，根据 SHAP 值，GDF5、BAHCC1、LCN2、FGF14-AS2 和 IDH2 属于最有影响力的前 5 个 mRNA。其中包括 GDF5、BAHCC1、LCN2、FGF14-AS2 和 IDH2 在内的突出鉴定的 mRNA 均与此相关。在淋巴结转移中。这项研究有望阐明对关键候选基因的全面了解，这些基因可能对乳腺癌患者淋巴结转移的早期检测和定制治疗策略产生重大影响。版权所有：© 2024 Vahed 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes.Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases.In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values.The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.Copyright: © 2024 Vahed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.