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从单细胞种群中预测新出现的表型,使用CELLECTION

Hongru Hu^1,1,2, Siddhant Sanghi^1,1, Gerald Quon^1,2

¹Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, 95616, USA.

bioRxiv : the preprint server for biology

|September 15, 2025

概括

我们开发了CELLECTION,这是一个深度学习框架,用于识别细胞和遗传特征. 这种方法将生物实例的子组与疾病,发育和进化研究的新兴表型联系起来.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
发展生物学发展生物学

背景情况:

生物系统显示出从集体成分行为中出现的现象型.
在遗传学和发育生物学等领域,了解这些新出现的特性是关键.

研究的目的:

引入CELLECTION,这是一个深度学习框架,用于将实例子组与新出现的表型联系起来.
为了证明CELLECTION在各种生物研究领域的实用性.

主要方法:

开发了一个名为CELLECTION的深度学习框架.
应用CELLECTION对异质任务,包括疾病分类和进化分析.

主要成果:

CELLECTION能够对复杂的生物任务进行可解释的预测.
成功应用于识别与疾病相关的细胞亚型和调整发育阶段.
用于预测鸟类相对手翼指数,展示进化见解.

结论:

CELLECTION提供了一种可扩展和灵活的方法来发现生物洞察力.
识别关键的细胞或遗传特征,这些特征是复杂特征的基础.
促进发展,疾病和进化生物学方面的研究.

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