机器学习在瘤药物基因组学:一条通往精准医学的道路,有许多挑战
Alessia Mondello1, Michele Dal Bo1, Giuseppe Toffoli1
1Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO), Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Aviano, Italy.
Frontiers in pharmacology
|January 24, 2024
概括
下一代测序 (NGS) 和机器学习 (ML) 正在改变癌症研究和药物基因组学 (PGx). 这些技术有助于分析复杂的遗传数据,以推进个性化癌症医学.
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.
- 计算生物学 计算生物学
背景情况:
- 在过去的20年里,下一代测序 (NGS) 显著推进了癌症研究.
- NGS 能够识别影响癌症病理生物学,诊断,预后和治疗的瘤特异性变化.
- 药物基因组学 (PGx) 研究个体遗传变异如何影响药物反应,利用高通量NGS数据.
研究的目的:
- 在药物基因组学 (PGx) 中提供下一代测序 (NGS) 方法的全面审查.
- 探索使用NGS数据的各种PGx研究.
- 通过PGx和NGS数据分析,研究机器学习 (ML) 算法在推进个性化癌症医学方面的作用.
主要方法:
- 关于NGS在癌症研究和PGx中的应用现有文献的综述.
- 使用NGS数据对现有的PGx研究进行分析.
- 关于与PGx数据分析相关的机器学习 (ML) 算法的研究.
主要成果:
- NGS为了解瘤变化和指导临床决策提供了关键数据.
- 机器学习算法正在成为复杂的NGS数据集中的模式发现的强大工具.
- NGS和ML的整合对瘤学中的个性化医学具有重大前景.
结论:
- 在现代癌症研究和药物基因组学中,NGS是基石技术.
- 机器学习对于释放PGx中NGS数据的全部潜力至关重要.
- 结合NGS和ML策略是改善个性化癌症治疗和患者治疗结果的关键.
关键词:
药物的有效性 药物的有效性药物重用是为了改变药物的用途.毒性毒性毒性毒性毒性毒性毒性毒性毒性毒性毒机器学习是机器学习.俄米克斯 (omicsics) 是一个电子产品.药物基因组学 药物基因组学有针对性的治疗.更多相关视频
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