EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Aimin Li, Mingyue Li, Rong Fei, Saurav Mallik, Bo Hu, Yue Yu
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引用次数: 0

Abstract

Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a large amount of scRNA-seq data, which can be analyzed to explore the regulatory relationships between genes at the single-cell level. Previous models used to construct GRNs mainly aim at constructing associative relationships between genes, but usually fail to accurately reveal the causality between genes. Therefore, we present a hybrid deep learning model called EfficientNet-resDDSC (the EfficientNet with Residual Blocks and Depthwise Separable Dilated Convolutions) to infer causality between genes. The model inherits the basic structure of EfficientNet-B0 and incorporates residual blocks as well as dilated convolutions. The model's ability to extract low-level features at the primary stage is enhanced by introducing residual blocks. The model combines Depthwise Separable Convolution (DSC) in the inverted linear bottleneck layers with the dilated convolutions to expand the model's receptive fields without increasing the computational effort. This design enables the model to comprehensively reveal potential relationships among different genes in high-dimensional and high-noise single-cell data. In comparison with the five existing deep learning network models, EfficientNet-resDDSC's overall performance is significantly better than others on four datasets. In this study, EfficientNet-resDDSC was further applied to construct GRNs for breast cancer patients, focusing on the related regulatory genes of the key gene BRCA1, which contributes to the advancement of breast cancer research and treatment strategies.

EfficientNet-resDDSC:整合残差块和稀释卷积的混合深度学习模型,用于推断单细胞数据中的基因因果关系。
基因调控网络(GRN)揭示了生物体内基因之间复杂的相互作用,对于理解生命系统的运行至关重要。生物技术的飞速发展,尤其是单细胞 RNA 测序(scRNA-seq)技术的发展,产生了大量的 scRNA-seq 数据,通过分析这些数据可以探索单细胞水平上基因之间的调控关系。以往用于构建 GRN 的模型主要旨在构建基因之间的关联关系,但通常无法准确揭示基因之间的因果关系。因此,我们提出了一种名为EfficientNet-resDDSC(带有残差块和深度可分离稀释卷积的EfficientNet)的混合深度学习模型来推断基因之间的因果关系。该模型继承了 EfficientNet-B0 的基本结构,并加入了残差块和扩张卷积。通过引入残差块,该模型在初级阶段提取低级特征的能力得到了增强。该模型将倒置线性瓶颈层中的深度可分离卷积(DSC)与扩张卷积相结合,在不增加计算量的情况下扩大了模型的感受野。这种设计使模型能够全面揭示高维、高噪声单细胞数据中不同基因之间的潜在关系。与现有的五个深度学习网络模型相比,EfficientNet-resDDSC 在四个数据集上的总体性能明显优于其他模型。本研究进一步将EfficientNet-resDDSC应用于构建乳腺癌患者的GRN,重点研究了关键基因BRCA1的相关调控基因,为乳腺癌研究和治疗策略的推进做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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