A hybrid ensemble-based parallel learning framework for multi-omics data integration and cancer subtype classification

Integrating multi-omics data to understand biological processes in human diseases is a complex bioinformatic task. Machine learning (ML), particularly deep learning (DL) models, offers a promising approach to multi-omics data integration and analysis. However, existing DL models generally integrate multi-omics data by concatenating the input data space or learned feature space, which is a sub-optimal approach. In addition, single classifiers are commonly used in DL-based methods, which can compromise the performance. Furthermore, the gradient descent optimization technique in DL suffers from a high computational cost and local sub-optimal solutions. To address these challenges, this article presents a novel cancer subtype classification framework using multi-omics integration and an ensemble-based parallel DL/ML architecture. Specifically, a multimodal autoencoder is used for effective feature learning across omics types, overcoming the limitations of naïve concatenation. A hybrid ensemble model comprising DL and ML learners with a meta-learner enhances classification robustness beyond single models. To improve optimization and computation, we incorporate a hybrid Back-Propagation and Particle Swarm Optimization (PSO) strategy and execute the entire framework on a parallel processing platform, reducing computation time while enhancing global search capability. The proposed framework is evaluated empirically with two benchmark data sets from The Cancer Genome Atlas (TCGA), namely the TCGA Pan-cancer and TCGA Breast Invasive Carcinoma (BRCA) data sets. The results indicate a high performance with accuracy rates of 89.51% and 90.9% for TCGA Pan-cancer and TCGA BRCA, respectively. The parallel implementation of the proposed framework reduces the computation time, resulting in a speed-up of 3 times and 2.5 times for TCGA Pan-cancer and TCGA BRCA, respectively. The findings ascertain the efficacy of the proposed framework for the classification of cancer subtypes, offering a promising solution for implementation in real-world environments.