{"title":"人工智能与基于质谱的多模态分子剖析方法,促进传染病的治疗发现。","authors":"","doi":"10.1016/j.pharmthera.2024.108712","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":402,"journal":{"name":"Pharmacology & Therapeutics","volume":null,"pages":null},"PeriodicalIF":12.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases\",\"authors\":\"\",\"doi\":\"10.1016/j.pharmthera.2024.108712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. 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引用次数: 0
摘要
由各种病原体引起的传染病会迅速破坏公共卫生系统。传染病的准确诊断和治疗以生物标志物的鉴定和疾病机理的阐明为中心,亟需更多用途和实用的分析方法。以质谱(MS)为基础的分子剖析方法可以提供包括核酸、蛋白质和代谢物在内的一系列功能分子的大量信息。虽然 MS 驱动的全息分析可以产生庞大的数据集,但 MS 数据的复杂性和多维性会严重阻碍对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)作为一种有效的补充工具,可以大大提高 MS 数据的处理和解释能力。人工智能在这方面的应用可减少虚假信号、提高精确度、创建标准化分析框架并提高数据整合效率。这篇重要综述强调了基于 MS 的全局策略在发现生物标记物和阐明传染病方面的关键作用。此外,本综述还强调了人工智能技术的变革能力,即通过改进全局分析产生的数据的质量和实用性,提高基于 MS 的分子剖析在传染病领域的实用性。总之,我们主张采取前瞻性战略,将人工智能与基于 MS 的分子图谱分析相结合。这种整合的目的是改变生物分子表征的分析格局和性能,有可能达到单细胞水平。预计这种进步将推动人工智能驱动的预测模型的发展,从而改善诊断监测和治疗发现,应对与传染病有关的持续挑战。
Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases
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.
期刊介绍:
Pharmacology & Therapeutics, in its 20th year, delivers lucid, critical, and authoritative reviews on current pharmacological topics.Articles, commissioned by the editor, follow specific author instructions.This journal maintains its scientific excellence and ranks among the top 10 most cited journals in pharmacology.