Vol. 5 No. 1 (2025): Issue 5
Articles

Gaussian Mixture Models-based Efficient Multi-Omics Data Integration

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  • Giovanni Rossi,
  • Elena Bianchi,
  • Marco Verdi
Giovanni Rossi
Department of Computational Biology, University of Trieste, Trieste, 34127, Italy
Elena Bianchi
Institute of Systems Analysis and Bioinformatics, University of L'Aquila, L'Aquila, 67100, Italy
Marco Verdi
Center for Integrative Genomics, University of Eastern Piedmont, Alessandria, 15121, Italy
DOI: https://doi.org/10.71070/jcbm.v5i1.67

Published 2025-02-10

Keywords

  • Multi-Omics,
  • Data Integration,
  • Gaussian Mixture Models,
  • Biological Insights,
  • Methodologies Advancement

How to Cite

Rossi, G., Bianchi, E., & Verdi, M. (2025). Gaussian Mixture Models-based Efficient Multi-Omics Data Integration. Journal of Computational Biology and Medicine, 5(1). https://doi.org/10.71070/jcbm.v5i1.67

Abstract

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.

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