摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-药物和治疗的新研究-个性化医疗是一篇报道的主题。根据马来西亚雪兰莪的新闻报道，NewsRx编辑的研究表明：“-本文提出了一项针对乳腺癌亚型的综合研究，采用了一种综合特征选择、机器学习分类器和miRNA调节网络的多种方法，特征选择过程从CFS算法开始，然后是关联规则生成的Apriori算法。”从而确定适合腔A、腔B、HER-2富集和基底样亚型的显著特征。我们的新闻记者从Tunku Abdul Rahman大学的研究中获得了一句话：“随机森林(RF)和支持向量机(SVM)分类器的后续应用取得了很好的结果，SVM模型的总体准确率为76.60%，RF模型的鲁棒性为80.85%。详细的精度指标显示了需要改进的优势和领域。”强调优化G亚型特异性回忆的潜力。为了深入探索调控格局，使用MIENTURNET对选定的miRNA进行了分析，MIENTURNET是一种可视化M IRNA-靶相互作用的工具。虽然FDR分析引起了HER-2和基础样亚型的关注，Luminal A和Luminal B亚型显示了显著的miRNA-基因E相互作用。Luminalhsa-let-7c-5p和hsa-miR-125b-5p等特异的miRNA可能作为Luminal A乳腺癌的诊断标志物和调节因子。Luminal B分析揭示了MAPK信号通路的联系，hsa-miR-203a-3p和hsa-miR-19a-3p等miRNA显示出潜在的诊断和治疗意义。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting ou t of Selangor, Malaysia, by NewsRx editors, research stated, "- This paper prese nts a comprehensive study focused on breast cancer subtyping, utilizing a multif aceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CF S algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, L uminal B, HER-2 enriched, and Basal-like subtypes." Our news journalists obtained a quote from the research from the University of T unku Abdul Rahman, "The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstr ating robust performance at 80.85 %. Detailed accuracy metrics reve aled strengths and areas for refinement, emphasizing the potential for optimizin g subtype-specific recall. To explore the regulatory landscape in depth, an anal ysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing m iRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal -like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gen e interactions. Functional enrichment analysis for Luminal A highlighted the rol e of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signal ing pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting pote ntial diagnostic and therapeutic significance."
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