A Fusion-Based Multiomics Classification Approach for Enhanced Gene Discovery in Non-Small Cell Lung Cancer

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 data sets. 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. The decision-level fusion-based approach yielded a superior classification performance as compared to the performance achieved by models trained on individual omics data sets. 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.