(Pre)Clinical Metabolomics Analysis

Pebriana, Ratna Budhi; Sánchez-López, Elena; Giera, Martin

doi:10.1007/978-1-0716-4116-3_1

Methods in Molecular Biology

2024

DOI: 10.1007/978-1-0716-4116-3_1

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(Pre)Clinical Metabolomics Analysis

Ratna Budhi Pebriana,

Elena Sánchez-López,

Martin Giera

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From multi-omics to predictive biomarker: AI in tumor microenvironment

Hai,

Jiang,

Zhang

et al. 2024

Front. Immunol.

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In recent years, tumors have emerged as a major global health threat. An increasing number of studies indicate that the production, development, metastasis, and elimination of tumor cells are closely related to the tumor microenvironment (TME). Advances in artificial intelligence (AI) algorithms, particularly in large language models, have rapidly propelled research in the medical field. This review focuses on the current state and strategies of applying AI algorithms to tumor metabolism studies and explores expression differences between tumor cells and normal cells. The analysis is conducted from the perspectives of metabolomics and interactions within the TME, further examining the roles of various cytokines. This review describes the potential approaches through which AI algorithms can facilitate tumor metabolic studies, which offers a valuable perspective for a deeper understanding of the pathological mechanisms of tumors.

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From multi-omics to predictive biomarker: AI in tumor microenvironment

Hai,

Jiang,

Zhang

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

show abstract

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(Pre)Clinical Metabolomics Analysis

Cited by 1 publication

References 70 publications

From multi-omics to predictive biomarker: AI in tumor microenvironment

From multi-omics to predictive biomarker: AI in tumor microenvironment

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