解读基于集体深度学习和从单细胞转录基因数据的联合评分策略的联体受体介导细胞间通信.
Lihong Peng1, Jingwei Tan2, Wei Xiong2
1School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.
Computers in biology and medicine
|June 26, 2023
概括
本研究介绍了CellComNet,这是一个深度学习框架,用于从单细胞数据中推断细胞-细胞通信. CellComNet准确地识别了连体受体相互作用,有助于理解瘤进展和开发向疗法.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 细胞与细胞之间的沟通在瘤微环境中至关重要,影响癌症的生长,进展和转移.
- 了解细胞间通信有助于破译驱动这些过程的分子机制.
研究的目的:
- 开发一个集体深度学习框架,CellComNet,从单细胞转录组数据推断联体受体介导的细胞-细胞通信.
- 为了提高癌症研究细胞间通信推断的准确性.
主要方法:
- CellComNet集成了数据处理,特征提取,维度缩小和分类,使用混合牛顿增强和深度神经网络模型.
- 通过单细胞RNA测序 (scRNA-seq) 数据识别和选带受体相互作用 (LRIs).
- 通过结合scRNA-seq数据,选的LRIs和联合评分策略来推断细胞-细胞通信.
主要成果:
- 在LRI分类方面,CellComNet的表现优于现有的模型,在多个数据集上实现了优异的AUC和AUPR.
- 对黑色素瘤和头支状细胞癌 (HNSCC) 的应用揭示了重要的传播途径:与癌症相关的纤维细胞与黑色素瘤细胞,以及带有HNSCC细胞的内皮细胞.
结论:
- 细胞ComNet有效地识别可信的LRI,并改善细胞-细胞通信推断.
- 该框架有可能推进抗癌药物设计和开发瘤向疗法.
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