A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer

Abstract Motivation This study introduces a fusion-based multiomics approach to identifying non-small cell lung cancer (NSCLC)-relevant genes. We evaluated the NSCLC-subtype classification performance of various state-of-the-art machine learning models using single omics and fused multiomics approaches. The models were trained separately on individual omics datasets. Subsequently, a weighted-average-based decision-level fusion mechanism was employed to integrate the individual predictions of the trained models. Finally, the prediction performance across all the approaches was compared. Results The decision-level fusion-based approach yielded a superior classification performance as compared to the performance achieved by models trained on individual omics datasets. Finally, a set of 47 NSCLC-relevant genes were identified. For the first time, ABCF3, ACAP2, LSG1, TBCCD1, UCN2, WDR53, ZNF639 and FYTTD1 appeared in the context of NSCLC. In conclusion, the integration of multiple omics types showed potential to deliver a more concise selection of NSCLC-relevant genes that could be clinically targeted in future. Availability and implementation Data and source code are available on: https://github.com/kountaydwivedi/multiomics_fusion.git.