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机器学习在乳腺癌分子病理学中的作用:对基因表达造型和RNA测序应用的审查

Sahar Rezaei¹, Zeinab Hamedani², Kousar Ahmadi³

¹Department of Nuclear Medicine, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran.

Critical reviews in oncology/hematology

|May 26, 2025

概括

人工智能 (AI) 通过分析基因表达数据以改善诊断和治疗来增强乳腺癌研究. 先进的机器学习技术在识别生物标志物和个性化患者护理方面表现有前途.

科学领域:

在瘤学瘤学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

乳腺癌仍然是一个重要的全球健康问题,发病率和死亡率不断增加.
由于乳腺癌的复杂性,创新的诊断和管理工具至关重要.
基因签名和生物技术方法显示出希望,但人工智能 (AI) 提供了一种革命性的数据分析方法.

研究的目的:

探索先进的机器学习技术,用于分析乳腺癌研究中的转录基因数据.
专注于使用AI进行乳腺癌亚型分类.
评估AI在乳腺癌管理中的潜在影响和局限性.

主要方法:

在主要的科学数据库 (PubMed,Scopus,WoS,Embase,IEEE Xplore) 进行了全面的文献搜索.
研究被选,并提取了关于分子方法,人工智能技术和临床应用的数据.
相关研究被分类为RNA测序和基因表达造型组.

主要成果:

在最初的7287篇文章中,保留了54篇进行审查 (24篇RNA测序,30篇基因表达概况).
人工智能,特别是诸如Random Forest,CNN,SVM和LASSO之类的算法,显著提升了乳腺癌研究.
人工智能技术在识别生物标志物,预测生存率和优化药物反应方面显示出潜力.

结论:

人工智能方法具有巨大的潜力,可以改变乳腺癌的诊断,预后和治疗.
目前的进展表明,人工智能在瘤学中的未来充满希望.
更大规模的研究和跨学科的合作对于进一步的进步至关重要.

关键词:

人工智能的人工智能乳腺癌乳腺癌乳腺癌基因表达简介基因表达简介分子病理学分子病理学有关RNA测序的研究.

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