Ensemble of Heterogeneous Machine Learning Models with Multiple Inputs for Multi-Omics Analysis

Multiple myeloma is a plasma cell neoplasm with genetic complexity that originates in pre-malignant stages due to genomic alterations, leading to malignant plasma cell proliferation. The completeness of data is significantly affecting multi-omics studies since the more sources included in the analysis, the more likely it is for key data to be missing. In this study, an ensemble meta-model that uses transfer learning from multiple single-source models was developed to assess the progression of multiple myeloma by leveraging radiocytogenetics. The proposed meta-model achieved the highest performance with an AUC of 0.75±0.07 and a SP of 0.84±0.02 among other single-source and radiocytogenetic models.Clinical Relevance: This study expands the current ensemble methods by allowing the combination of pre-trained machine learning models with multiple inputs for MM radiocytogenetics.