一个全面的机器学习,用于高吞吐量结核病序列分析,功能注释和可视化.
Md Saddam Hossain1, Md Parvez Khandocar2, Farzana Akter Riti2
1Department of Biomedical Engineering, Faculty of Engineering and Technology, Islamic University, Kushtia, 7003, Bangladesh. saddam.iu.bme@gmail.com.
Scientific reports
|July 16, 2025
概括
机器学习模型使用RNA测序数据准确地对结核病患者进行分类. XGBoost实现了最高的准确性,识别了潜在的结核病诊断和治疗的关键基因和途径.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 结核病 (TB) 是全球主要的健康威胁,每年造成超过一百万人的死亡.
- 准确和早期诊断结核病对于有效的治疗和疾病控制至关重要.
- 机器学习 (ML) 为分析复杂的生物数据提供了强大的工具,包括RNA序列.
研究的目的:
- 开发和评估ML监督的算法,用于从RNA序列计数数据中对结核病患者进行分类.
- 通过特征重要性技术识别与结核病相关的重要基因和生物途径.
- 通过先进的计算分析来探索潜在的治疗点和药物相互作用.
主要方法:
- 在大型RNA序列计数数据上训练了多个ML算法 (XGBoost,后勤回归,随机森林,AdaBoost,SVM).
- 利用XGBoost的特征重要性分析来识别与结核病相关的关键基因.
- 应用途径分析,基因本体学 (GO),枢纽蛋白和蛋白质-蛋白质相互作用 (PPI) 网络分析,以及药物-蛋白质相互作用分析.
主要成果:
- 对于结核病分类,XGBoost实现了0.963的最高预测准确度.
- 确定了与结核病相关的20个显著途径,24个基因本体,20个枢纽基因和22种潜在药物.
- 对100个高度表达的基因的分析揭示了复杂的分子相互作用.
结论:
- ML模型,特别是XGBoost,在根据RNA-seq数据对结核病患者进行分类方面表现出高效.
- 鉴定的基因,途径和潜在药物为结核病诊断和治疗开发提供了宝贵的见解.
- 这项研究强调了将ML与多omics数据集成为推进结核病研究的潜力.
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