Oncotree2vec - - 一种嵌入和集群瘤突变树的方法
Monica-Andreea Baciu-Drăgan1,2, Niko Beerenwinkel1,2
1Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, Basel 4056, Switzerland.
Bioinformatics (Oxford, England)
|June 28, 2024
概括
我们介绍了oncotree2vec,这是一种新的方法,可以使用无监督学习对瘤突变树进行聚类. 这种方法有效地根据进化关系对瘤进行分组,有助于个性化癌症治疗的发现.
科学领域:
- 计算瘤学是一种计算瘤学.
- 癌症基因组学 癌症基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 瘤基因组异质性对于个性化癌症治疗至关重要.
- 集群瘤突变树,考虑到遗传事件顺序,有助于识别瘤亚型并改善诊断/预后.
研究的目的:
- 开发oncotree2vec,一种对瘤突变树进行无监督聚类的方法.
- 学习突变树的向量表示,以捕捉子克隆关系.
- 为了促进大型瘤队列的可视化和下游分析.
主要方法:
- 建议oncotree2vec用于学习突变树的低维向量嵌入.
- 采用无监督学习来捕捉瘤亚克隆之间的关系.
- 通过模拟研究和现实癌症数据集验证了该方法.
主要成果:
- oncotree2vec成功地根据进化模式对瘤突变树进行了聚类.
- 学习嵌入使得在大型患者队伍中可视化树关系.
- 在各种癌症类型和急性髓性白血病 (AML) 数据集上证明有用.
结论:
- oncotree2vec为分析瘤演变和异质性提供了一个强大的工具.
- 该方法支持下游应用,包括多omics数据集成.
- 能够更深入地了解瘤亚型,从而改善临床应用.
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