多组学研究:综合分析的原则和挑战。

Q2 Agricultural and Biological Sciences
生物设计研究(英文) Pub Date : 2024-12-05 eCollection Date: 2024-01-01 DOI:10.34133/bdr.0059
Yunqing Luo, Chengjun Zhao, Fei Chen
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引用次数: 0

摘要

多组学研究是生物科学中的一种变革性方法,它集成了基因组学、转录组学、蛋白质组学、代谢组学和其他组学技术的数据,以提供对生物系统的全面理解。本文综述了多组学的基本原理,强调了数据整合的必要性,以揭示各种生物过程背后复杂的相互作用和调控机制。我们探索了计算方法的最新进展,包括深度学习、图神经网络(gnn)和生成对抗网络(gan),这些方法有助于有效地综合和解释多组学数据。此外,本文还讨论了该领域的关键挑战,例如数据异构性、可伸缩性以及对健壮的、可解释的模型的需求。我们强调了大型语言模型通过自动特征提取、自然语言生成和知识集成来增强多组学分析的潜力。尽管多组学有着重要的前景,但该综述承认需要大量的计算资源和模型调整的复杂性,强调了该领域持续创新和合作的必要性。这一综合分析旨在指导研究人员在导航的原则和挑战的多组学研究,以促进综合生物学分析的进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiomics Research: Principles and Challenges in Integrated Analysis.

Multiomics research is a transformative approach in the biological sciences that integrates data from genomics, transcriptomics, proteomics, metabolomics, and other omics technologies to provide a comprehensive understanding of biological systems. This review elucidates the fundamental principles of multiomics, emphasizing the necessity of data integration to uncover the complex interactions and regulatory mechanisms underlying various biological processes. We explore the latest advances in computational methodologies, including deep learning, graph neural networks (GNNs), and generative adversarial networks (GANs), which facilitate the effective synthesis and interpretation of multiomics data. Additionally, this review addresses the critical challenges in this field, such as data heterogeneity, scalability, and the need for robust, interpretable models. We highlight the potential of large language models to enhance multiomics analysis through automated feature extraction, natural language generation, and knowledge integration. Despite the important promise of multiomics, the review acknowledges the substantial computational resources required and the complexity of model tuning, underscoring the need for ongoing innovation and collaboration in the field. This comprehensive analysis aims to guide researchers in navigating the principles and challenges of multiomics research to foster advances in integrative biological analysis.

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来源期刊
CiteScore
3.90
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