MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery
Wei Lan, Zhentao Tang, Haibo Liao, Qingfeng Chen, Yi‐Ping Phoebe Chen, Zhaolei Zhang, Jianxin Wang
Abstract
Multi-omics cancer data provides complementary views of tumorigenesis and progression. Technical challenges exist in integrating these heterogeneous data into deep learning models to better understand tumorigenesis and predict cancer recurrence. We herein propose a novel end-to-end deep learning method (MULGONET) for cancer recurrence prediction and biomarker discovery. First, MULGONET can effectively solve the curse of dimensionality and the lack of model interpretability in multi-omics data integration. Second, it explores interactions and regulatory relationships between genes and GO terms, thus providing biological insights. Benchmark results show that MULGONET outperforms other contemporary classification methods. It achieves AUPRs of 0.774 ± 0.015, 0.873 ± 0.003 and 0.702 ± 0.011 on the bladder, pancreatic and stomach cancer datasets, respectively. We also show that MULGONET can effectively identify prognostic genes and GO terms associated with cancer recurrence.