Brief project description 

Data integration is essential for extracting knowledge from diverse multi-omics data - high-throughput data from biological systems - to improve predictions and understanding of complex biological activities and provide a clearer view of the observed data. The term "omics data integration" describes mathematical models, algorithms, and software that combine the information available in multiple omics data matrices into a more comprehensive view. Moreover, combining these diverse data sets can reveal patterns related to specific conditions. However, challenges such as data heterogeneity, high dimensionality, limited sample size, and high levels of noise need to be addressed.

This project aims to develop effective and novel computational methods for multi-omics data integration, combining statistical methods, optimization approaches, and matrix factorization to exploit the latest research results. The proposed methods will be implemented as open-source packages, focusing on scalability and medical interpretability.  The project's strategic importance lies in its potential to impact precision medicine and advance mathematical frameworks for integrating heterogeneous data, thus contributing significantly to the strategic topic of Human well-being.