International Journal of Molecular Medical Science, 2024, Vol.14, No.1, 90-99 http://medscipublisher.com/index.php/ijmms 93 monitoring treatment responses. By comparing metabolomics data from different samples, changes in metabolic pathways and key nodes in metabolic networks can be revealed. This helps in understanding the regulatory mechanisms of metabolism, the regulation of biological processes, and the mechanisms of disease occurrence. Metabolomics data can provide information about drug metabolism pathways in the body, drug metabolites, and pharmacokinetics. This is significant for drug development and personalized drug therapy. Metabolomics data can be used to analyze the effects of different foods or dietary patterns on metabolism, helping to understand the relationship between food and health and providing scientific evidence for personalized nutritional guidance. 2 Methods and Tools for Multi-omics Data Integration 2.1 Main methods of data integration The main methods of data integration include clustering analysis and phenotypic clustering, association and network analysis, and machine learning and artificial intelligence methods. In practical applications, different methods and tools are often combined for multi-omics data integration and analysis to comprehensively understand the complexity and interrelationships of biological systems. Clustering analysis is a method of grouping samples or features in a dataset based on their similarity. It can be used to cluster samples from different omics data to discover potential biological clustering patterns. Phenotypic clustering involves clustering phenotypic information (such as gene expression, protein expression, etc.) from multi-omics data and grouping samples with similar phenotypes into the same cluster. This helps in understanding the associations between different data types and their combined impact on biological characteristics. Association analysis is used to find correlations or association patterns in multi-omics data. For example, correlation coefficients or mutual information metrics can be used to evaluate the correlation between different data and find related features. Network analysis methods consider biological molecules (such as genes, proteins, metabolites) in multi-omics data as nodes of a network, with edges representing their interactions. This helps to reveal regulatory mechanisms and biological associations in multi-omics data. Machine learning and artificial intelligence methods can be used to integrate multi-omics data and discover patterns and rules in the data. For example, supervised learning algorithms (such as support vector machines, random forests) can be used for classification and prediction, while unsupervised learning algorithms (such as clustering, dimensionality reduction) can be used for data exploration and pattern discovery. Deep learning, a branch of machine learning, uses multi-layer neural network models to learn complex features and associations in multi-omics data. It has achieved great success in fields such as image recognition and natural language processing and is also applied to data integration and analysis in biology (Liu et al., 2022). 2.2 Common software tools and databases for data integration There are several commonly used software tools and databases available for data integration. For databases and datasets, the NCBI database provides various biomedical data resources, the Ensembl database is a comprehensive genome annotation database, and the GEO database contains high-throughput transcriptomic data from around the world. Additionally, the TCGA database is a rich collection of cancer multi-omics data. For data analysis and integration tools, R/Bioconductor is a popular language for statistical computing and data visualization, with packages such as limma, edgeR, DESeq2, and ConsensusClusterPlus available for multi-omics data analysis. Python is also widely used, with packages like pandas, numpy, scikit-learn, and TensorFlow suitable for data integration and analysis. Cytoscape is a powerful platform for network analysis and visualization, suitable for integrating multi-omics data and revealing relationships between biological molecules. The GSEA tool can be used to determine the enrichment of gene sets in different omics data. Additionally, Galaxy is a scientific workflow management system that can integrate and analyze multi-omics data (Afgan et al., 2018). These tools and databases are just some of the common choices, and suitable software tools and databases can be selected based on specific research needs and data types in practical use.
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