Unlocking the secrets of metabolomics with Artificial Intelligence: a comprehensive literature review

This comprehensive review synthesizes the wealth of scientific literature pertaining to the application of Artificial Intelligence (AI) in the field of metabolomics. Over the past decade, AI has played an increasingly pivotal role in deciphering the complexities of metabolomic data, offering novel insights into the molecular underpinnings of biological systems. Through an extensive examination of relevant research papers, we provide a comprehensive overview of the diverse AI techniques and methodologies, from data preprocessing and feature selection to predictive modeling and pathway analysis, employed in metabolomics studies. The review dissects key trends and advancements in AI-driven metabolomics, shedding light on its pivotal role in biomarker discovery, disease diagnosis, and personalized medicine. In addition to highlighting the significant contributions of AI to metabolomics, emerging frontiers will be explored, such as the incorporation of multi-omics data integration and the growing importance of explainable AI in biological research. Ultimately, this review underscores the transformative impact of AI on metabolomics, emphasizing its potential to reshape our understanding of metabolic pathways, disease mechanisms, and therapeutic interventions. The combination of AI and metabolomics stands as a powerful paradigm shift with far-reaching implications for advancing both fundamental scientific knowledge and practical applications across diverse domains

Overview of Sepsis and Sepsis Biomarker Detection

Sepsis being a fatal physiological state due to an imbalance in the immune system caused by infection, and one of the most common cause for millions of deaths in the non-coronary intensive care unit worldwide requires special attention in its diagnostic methods and cure. Therefore an understanding of literature related to sepsis is of utmost importance. With the advent of inter-disciplinary research, the study and diagnosis of sepsis problem are not limited to the medical field, rather it requires interventions and active participation of other fields of science and technology. However, often subject matter from interdisciplinary research is expounded in an abstruse manner and hence it becomes elusive for a researcher from different research domain to understand it, leading to loss of quality and efficiency in research. In this survey report, the material is presented in a form that facilitates easy comprehension for the non-medical researchers and has been focused on introducing sepsis, it\u27s causes, extent, comparison of diagnosis techniques: conventional labeled and label-free detections; with special emphasis on sepsis biomarkers to help researchers from multi-disciplinary domain to develop and fabricate devices and ideas to compliment the existing sepsis diagnosis system present in the medical field. A future direction of sepsis diagnosis along with the implementation of novel techniques for sepsis biomarker quantification is also reported