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开发基于集群的方法来解读神经发育差异的个体中的复杂性

Tania Cuppens¹, Manpreet Kaur², Ajay A Kumar³

¹Département de Médecine Moléculaire de L'Université Laval, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, Canada.

Frontiers in pediatrics

|October 4, 2023

概括

像等级集群集群 (HAC) 和k-means这样的集群方法可以将全球发育迟缓 (GDD) 个体组合起来. 这种分组对于开发针对性治疗和未来针对GDD患者的篮子试验至关重要.

科学领域:

遗传学遗传学是一个
神经科学是一个神经科学.
计算生物学计算生物学

背景情况:

全球发育迟缓 (GDD) 呈现出显著的基因型和表型异质性,复杂化了针对性干预措施的开发.
对GDD个体遗传原因的稀有性阻碍了传统的临床试验设计.
在瘤学中成功的篮子试验为GDD研究提供了潜在的模型,但需要有效的患者分层.

研究的目的:

评估聚合和分裂集群方法对GDD个体分层的有用性.
识别潜在的患者子组,他们可以接受新的治疗策略,如篮子试验.
探索GDD中表型集群和潜在的遗传网络之间的关系.

主要方法:

使用解密发育障碍 (DDD) 队列,包括6,588名患有GDD的个人.
应用了对基因型和表型数据的等级聚合集群 (HAC) 和k-means集群 (分割方法).
在已识别的集群中分析基因网络和分子功能数据.

主要成果:

HAC在GDD个体中确定了16个表型集群,其特点是具有主导性的表型,如言语延迟/缺席和发作.
每个表型集群包含3-12个具有不同功能的分子特异性基因子网络.
K-means集群也根据表型分离了个体,但与HAC相比,确定了不同的遗传途径.

结论:

聚合和分裂的集群方法都可用于将GDD患者分组为未来的篮子试验.
将表型集群细分为分子子网络可能会提高治疗成功率.
对于全面的GDD治疗开发,可能需要采用聚合和分裂集群的综合方法.

关键词:

篮子试验篮子试验全球发展延迟层次化的聚合集群聚类.k-表示集群的平均值.神经发育方面的差异.现象型现象型是一种现象型.整体外基因组测序的测序

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