TY - JOUR
T1 - Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases
AU - Liu, Jingjing
AU - Bao, Chaohui
AU - Zhang, Jiaxin
AU - Han, Zeguang
AU - Fang, Hai
AU - Lu, Haitao
N1 - This work was supported by the Natural Science Foundation of Shanghai (Grant No. 21ZR1431600), the National Key Research and Development Program of China (No. 2017YFC1308600 and 2017YFC1308605), the key grant provided by National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (Shanghai, China) (Grant No. NRCTM(SH)-2021-07). The SKL-GRF Grant from State Key Laboratory of Environmental and Biological Analysis at Hong Kong Baptist University [SKLP_2425_P02].
Publisher Copyright:
© 2024 Elsevier Inc. All rights reserved.
PY - 2024/9/4
Y1 - 2024/9/4
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Infectious diseases
KW - Mass spectrometry
KW - Molecular profiling
KW - STORM+
KW - Therapeutic discovery
UR - http://www.scopus.com/inward/record.url?scp=85203405967&partnerID=8YFLogxK
U2 - 10.1016/j.pharmthera.2024.108712
DO - 10.1016/j.pharmthera.2024.108712
M3 - Journal article
C2 - 39241918
SN - 0163-7258
VL - 263
JO - Pharmacology and Therapeutics
JF - Pharmacology and Therapeutics
M1 - 108712
ER -