학술논문
Ensemble of Heterogeneous Machine Learning Models with Multiple Inputs for Multi-Omics Analysis
Document Type
Conference
Author
Source
2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology Data Science and Engineering in Healthcare, Medicine and Biology, 2023 IEEE EMBS Special Topic Conference on. :187-188 Dec, 2023
Subject
Language
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.