Modeling with multi-omics data presents multiple challenges such as the high-dimensionality of the problem (p≫n), the presence of interactions between features, and the need for integration between multiple data sources. We establish an interaction model that allows for the inclusion of multiple sources of data from the integration of two existing methods, pliable lasso and cooperative learning. The integrated model is tested both on simulation studies and on real multi-omics datasets for predicting labor onset and cancer treatment response. The results show that the model is effective in modeling multi-source data in various scenarios where interactions are present, both in terms of prediction performance and selection of relevant variables.
@article{arxiv.2409.07125,
title = {Integrating Multiple Data Sources with Interactions in Multi-Omics Using Cooperative Learning},
author = {Matteo D'Alessandro and Theophilus Quachie Asenso and Manuela Zucknick},
journal= {arXiv preprint arXiv:2409.07125},
year = {2024}
}