School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China.
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Pharmacol Ther. 2024 Nov;263:108712. doi: 10.1016/j.pharmthera.2024.108712. Epub 2024 Sep 4.
Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.
传染病由各种病原体驱动,会迅速破坏公共卫生系统。传染病的准确诊断和治疗,围绕着生物标志物的识别和疾病机制的阐明,迫切需要更具通用性和实用性的分析方法。基于质谱(MS)的分子分析方法可以提供大量关于各种功能分子的信息,包括核酸、蛋白质和代谢物。虽然 MS 驱动的组学分析可以产生大量数据集,但 MS 数据的复杂性和多维性极大地阻碍了特定生物过程和事件中功能分子的识别和表征。人工智能(AI)作为一种强大的补充工具,可以显著增强 MS 数据的处理和解释。在这种情况下,AI 应用可以减少虚假信号,提高精度,创建标准化的分析框架,并提高数据集成效率。这篇重要的综述强调了基于 MS 的组学策略在传染病生物标志物发现和阐明中的关键作用。此外,该综述强调了 AI 技术通过改进组学分析产生的数据的质量和实用性,增强 MS 基于分子分析在传染病领域应用的变革能力。总之,我们提倡采用一种前瞻性策略,将 AI 与基于 MS 的分子分析相结合。这种集成旨在改变分析领域和生物分子特征的性能,甚至可能达到单细胞水平。这些进展有望推动 AI 驱动的预测模型的发展,从而改善对诊断和治疗发现的监测,以应对当前传染病的挑战。