Overview of multi-omics research in gout.

Gout is a self-limiting inflammation disease triggered by deposition of monosodium urate with a variety of comorbidities. With the improvement of living standards, the global incidence of gout is increasing year by year, which seriously affects people's health. As an effective tool to study diseases, omics technology has been widely used to discover potential biomarkers and risk factors of gout. The identified variation sites or different-expressed products provide different dimensions of insights for the study of the pathogenesis and disease progression of gout. In this review, the application and research results of multi-omics technology in gout were analyzed and summarized through PubMed literature retrieval. Meanwhile, the recent research progress of multi-omics technology in the field of gout was reviewed to understand the specific changes of gout patients at different molecular levels, and to provide ideas and directions for further research on gout in the future.

Omics big data and medical artificial intelligence.

With the rapid development of high-throughput sequencing technology and computer science, the amount of large omics data has increased exponentially, the advantages of multi-omics analysis have gradually emerged, and the application of artificial intelligence has become more and more extensive. In this review, we introduce the application progress of multi-omics data analysis and artificial intelligence in the medical field in recent years, and also show the cases and advantages of their combined application. Finally, we briefly explain the current challenges of multi-omics analysis and artificial intelligence in order to provide new research ideas for the medical industry and to promote the development and application of precision medicine.

[Applications of machine learning in clinical decision support in the omic era].

With the development of the omic technologies, the acquisition approaches of various biological data on different levels and types are becoming more mature. As a large amount of data will be produced in the process of diagnosis and treatment of diseases, it is necessary to utilize the artificial intelligence such as machine learning to analyze complex, multi-dimensional and multi-scale data and to construct clinical decision support tools. It will provide a method to figure out rapid and effective programs in diagnosis and treatment. In this process, the choice of artificial intelligence seems to be particularly important, such as machine learning. The article reviews the type and algorithm of machine learning used in clinical decision support, such as support vector machines, logistic regression, clustering algorithms, Bagging, random forests and deep learning. The application of machine learning and other methods in clinical decision support has been summarized and classified. The advantages and disadvantages of machine learning are elaborated. It will provide a reference for the selection between machine learning and other artificial intelligence methods in clinical decision support.