Ensemble of Heterogeneous Machine Learning Models with Multiple Inputs for Multi-Omics Analysis
2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology(2023)
Abstract
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.
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Key words
Machine Learning Models,Ensemble Model,Heterogeneous Model,Multiple Myeloma,Transfer Learning,Plasmacytoma,Malignant Plasma Cells,Chromosomal Abnormalities,Radial Basis Function,Β2-microglobulin,Multiple Myeloma Patients,Early Fusion,Late Fusion,Relapsed Multiple Myeloma
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