基于深度学习的多组学数据整合与分析方法。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-10-02 DOI:10.1186/s13040-024-00391-z
Jenna L Ballard, Zexuan Wang, Wenrui Li, Li Shen, Qi Long
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引用次数: 0

摘要

背景:深度学习的迅猛发展以及不断增长的海量可用数据,为复杂和异构数据类型的融合与分析提供了充足的进步机会。不同的数据模式可以提供互补信息,利用这些信息可以更全面地了解每个主题。在生物医学领域,多组学数据包括分子(基因组学、转录组学、蛋白质组学、表观基因组学、代谢组学等)和成像(放射组学、病理组学)模式,这些模式结合在一起,有可能提高预测、分类、聚类和其他任务的性能。深度学习包含多种方法,每种方法在多组学整合方面都有一定的优缺点:在这篇综述中,我们按照基本架构对近期基于深度学习的方法进行了分类,并讨论了它们相互之间的独特能力。我们还讨论了推动多组学整合领域发展的一些新兴主题:基于深度学习的多组学整合方法大致分为非生成型(前馈神经网络、图卷积神经网络和自动编码器)和生成型(变异方法、生成对抗模型和生成预训练模型)。生成式方法的优势在于能够对共享表征施加约束,以强制执行某些属性或纳入先验知识。它们还可用于生成或估算缺失的模态。这些方法最近取得的进展包括能够处理不完整数据,以及超越传统的分子 omics 数据类型,整合成像数据等其他模态:我们希望看到能够处理缺失数据的方法进一步发展,因为这是处理复杂和异构数据时面临的共同挑战。此外,整合更多数据类型的方法有望通过捕捉每个样本的综合视图来提高下游任务的性能。
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Deep learning-based approaches for multi-omics data integration and analysis.

Background: The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration.

Method: In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration.

Results: Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data.

Conclusion: We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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