在多模态 Omics 数据整合中利用人工智能:为精准医学的下一个前沿领域铺平道路。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Yonghyun Nam, Jaesik Kim, Sang-Hyuk Jung, Jakob Woerner, Erica H Suh, Dong-Gi Lee, Manu Shivakumar, Matthew E Lee, Dokyoon Kim
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引用次数: 0

摘要

将多组学数据与电子健康记录中的详细表型分析整合在一起,标志着生物医学研究模式的转变,为人们提供了无与伦比的健康和疾病路径的整体视角。本综述描述了多模态组学数据整合的现状,强调了其在全面了解复杂生物系统方面的变革潜力。我们探讨了强大的数据整合方法,从基于连接的方法到基于转换和基于网络的策略,旨在利用不同数据类型的复杂细微差别。我们的讨论范围从纳入大规模群体生物库到剖析单细胞水平的高维 omics 层面。这篇综述强调了大型语言模型在人工智能中的新兴作用,预计它们的影响将在不久的将来成为数据整合方法的支点。在强调成就和障碍的同时,我们主张共同努力建立复杂的整合模型,为精准医学的突破性发现奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine.

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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