MoAGL-SA:一种利用图学习和自我关注进行癌症亚型分类的多组学自适应整合方法。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Lei Cheng, Qian Huang, Zhengqun Zhu, Yanan Li, Shuguang Ge, Longzhen Zhang, Ping Gong
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引用次数: 0

摘要

背景:通过深度学习整合多组学数据大大改善了癌症亚型分类,尤其是在特征学习和多组学数据整合方面。然而,将样本结构信息嵌入特征空间和设计灵活的整合策略仍是关键挑战:我们提出了基于图学习和自我关注的自适应多组学整合方法 MoAGL-SA,以应对这些挑战。首先,利用图学习从每个组学数据集生成患者关系图。然后,利用三层图卷积网络提取特定于 omic 的图嵌入。然后,利用自我关注来关注最相关的 omics,并自适应地为不同的图嵌入分配权重,以实现多组学整合。最后,使用软最大分类器对癌症亚型进行分类:实验结果表明,MoAGL-SA 在乳腺浸润癌、肾乳头状细胞癌和肾透明细胞癌数据集上的表现优于几种流行的算法。此外,MoAGL-SA 还成功识别了乳腺浸润癌的关键生物标记物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification.

Background: The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.

Results: We propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.

Conclusions: Experimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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