A Hybrid Ensemble-Based Parallel Learning Framework for Multi-Omics Data Integration and Cancer Subtype Classification

The code supports replication of results on TCGA Pan-cancer and BRCA datasets and includes data preprocessing, model training, and evaluation scripts:<br>Python scripts for data preprocessing and integrationAutoencoder implementation for multimodal feature learningHybrid ensemble training code (DL/ML models and meta-learner)PSO and backpropagation hybrid optimization codeParallel execution scriptsInstructions for replicating results on TCGA Pan-cancer and BRCA datasets

本代码可复现癌症基因组图谱（The Cancer Genome Atlas, TCGA）泛癌队列与乳腺浸润癌（Breast Invasive Carcinoma, BRCA）数据集上的实验结果，并涵盖数据预处理、模型训练与评估脚本：<br>用于数据预处理与整合的Python脚本<br>面向多模态特征学习的自编码器（Autoencoder）实现代码<br>混合集成训练代码（涵盖深度学习/机器学习模型与元学习器）<br>粒子群优化（Particle Swarm Optimization, PSO）与反向传播（Backpropagation）混合优化代码<br>并行执行脚本<br>针对TCGA泛癌队列与BRCA数据集的结果复现指南