通过人工智能和多omics分析,揭示可药物治疗的癌症驱动蛋白和向药物
Andrés López-Cortés1, Alejandro Cabrera-Andrade2,3, Gabriela Echeverría-Garcés4,5
1Cancer Research Group (CRG), Faculty of Medicine, Universidad de Las Américas, Quito, Ecuador. aalc84@gmail.com.
Scientific reports
|August 21, 2024
概括
我们开发了一种机器学习模型,以预测可药物治疗的癌症驱动蛋白质. 这种方法确定了79种关键蛋白质,并优先考虑了11种潜在的癌症治疗药物.
科学领域:
- 计算生物学和生物信息学
- 药物的发现和开发.
- 基因组学和癌症研究
背景情况:
- 识别可用药的蛋白质对于有效的癌症药物设计至关重要.
- 预测建模和in silico选加速了潜在治疗标的识别.
- 了解可用药物的蛋白质组有助于开发向癌症疗法.
研究的目的:
- 开发一种准确的预测分类器,用于可药物治疗的癌症驱动蛋白.
- 整合多主题数据和药物重定向,以提高目标优先级.
- 确定癌症治疗的新生物标志物和治疗点.
主要方法:
- 使用氨基酸组成描述器和机器学习分类器,特别是支持矢量机.
- 综合多主题数据,包括目标疾病的证据,癌症特征途径和蛋白质结合性.
- 进行药物重定向分析以识别针对优先级蛋白质的药物.
主要成果:
- 使用支持向量机器,实现了高性能,AUROC为0.975和精度为0.929.
- 鉴定出79种关键的可药物治疗的癌症驱动蛋白质,具有高度的结合性.
- 优先考虑23种具有不良预后意义的蛋白质和11种临床相关药物作为向.
结论:
- 开发的机器学习模型准确地预测了可用药物的癌症驱动蛋白质.
- 这一战略有效地优先考虑了临床研究中的生物标志物,治疗点和药物.
- 这些发现为开发新的向癌症治疗提供了基础.
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