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EfficientNet-resDDSC:一个混合深度学习模型,集成剩余块和扩展卷曲,用于在单细胞数据中推断基因因果关系

Aimin Li^1,2, Mingyue Li¹, Rong Fei^3,4

¹School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, 710048, China.

Interdisciplinary sciences, computational life sciences

|November 22, 2024

概括

一个新的深度学习模型,EfficientNet-resDDSC,准确地从单细胞RNA测序数据中推断基因因果关系. 这促进了基因调控网络的建设,以获得生物学见解和乳腺癌研究.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

基因调控网络 (GRNs) 对于理解生物系统至关重要.
单细胞RNA测序 (scRNA-seq) 为GRN分析产生了大量的数据.
现有的GRN模型往往无法确定基因因果关系.

研究的目的:

开发一种新的深度学习模型来推断基因因果关系.
为了提高基因调节网络建设的准确性.
将模型应用于乳腺癌患者数据.

主要方法:

推出了混合深度学习模型EfficientNet-resDDSC.
集成的残留块用于增强特征提取.
利用深度可分离的卷积与扩展的卷积来扩大受容场,而不会增加计算成本.

主要成果:

在4个数据集上,EfficientNet-resDDSC在5个现有的深度学习模型中表现出卓越的性能.
该模型有效地处理高维和杂的scRNA-seq数据.
成功应用于构建乳腺癌的GRNs,专注于BRCA1调节基因.

结论:

EfficientNet-resDDSC在从scRNA-seq数据中推断基因因果关系方面取得了重大进展.
该模型为构建精确的基因调节网络提供了强大的工具.
这项研究有助于更深入地了解乳腺癌和潜在的治疗策略.

关键词:

因果关系是因果关系.深度学习是一种深度学习.扩展的卷曲扩大了卷曲.剩余的块是剩余的块.这就是scRNA-seqq.

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