Abstract
Multi-omics analysis integrates data from different omics layers such as genomics, transcriptomics, proteomics, and metabolomics to gain a comprehensive understanding of biological systems. However, combining data from different sources presents several challenges, including differences in data types, dimensionality, and noise. This article reviews the key data integration strategies in multi-omics analysis, focusing on computational methods that facilitate the integration of heterogeneous omics datasets. We explore various statistical and machine learning approaches, including correlation-based methods, matrix factorization, and deep learning, highlighting their applications in systems biology, disease research, and personalized medicine.
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