Gaussian Mixture Models-based Efficient Multi-Omics Data Integration

In the realm of multi-omics data analysis, the integration of diverse biological datasets has become crucial for obtaining a comprehensive understanding of complex biological systems. Current research faces challenges in effectively combining different types of omics data due to differences in data structures and characteristics. This paper addresses these challenges by proposing a novel approach based on Gaussian Mixture Models for efficient multi-omics data integration. The innovative method presented in this study enables the seamless integration of varied omics data types, leading to more accurate and reliable biological insights. By leveraging the distinct advantages of Gaussian Mixture Models, this research contributes significantly to the advancement of multi-omics data analysis methodologies.