通过机器学习和深度学习方法探索登革热中潜在的基因特征
Jhansi Venkata Nagamani Josyula1,2, Shraddha Jangili1,2, Nikhila Yaladanda1,2
1Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.
Virus genes
|December 2, 2025
概括
这项研究确定了用于诊断登革热和严重登革热的关键基因,使用微阵列数据上的机器学习. 这些已识别的基因,特别是那些参与血小板功能的基因,显示出作为诊断生物标记物的前景.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 传染性疾病 传染性疾病
背景情况:
- 登革热构成了全球重大公共卫生挑战.
- 准确的诊断对于有效的患者管理至关重要.
研究的目的:
- 确定与登革热临床条件相关的差异表达基因 (DEGs).
- 使用先进的计算方法发现登革热的潜在诊断生物标志物.
主要方法:
- 对公开的微阵列数据集的分析 (GSE84331,GSE18090,GSE43777,E-MTAB-3162).这些数据集包括:
- 应用统计分析,机器学习 (ML) 和深度学习 (DL) 技术,包括随机森林和带有遗传算法 (SVM-GA) 的支持矢量机器.
- 功能丰富,血小板信号传递和蛋白质与蛋白质相互作用 (PPI) 网络分析.
主要成果:
- 在登革热 (DF) 与对照 (C) 中确定了27个DEG,在严重的登革热 (SD) 中确定了13个DEG与DF使用随机森林.
- 在SD与C中使用SVM-GA识别了79个DEG.
- 突出了七个枢纽基因 (PIK3R1,GATA3,ZFPM,SKAP1,TP63,ZBTB20,ZEB2) 在血液静止和血小板功能中具有潜在作用.
结论:
- 鉴定到的枢纽基因显示出潜在的登革热诊断标记物.
- 需要对更大的队列进行进一步验证,以确认它们的预后效用.
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