Uncertainty-induced Incomplete Multi-Omics Integration Network for Cancer Diagnosis

Cancer diagnosis with incomplete multi-omics data is inevitable since there are widely missing arbitrary omics in real-world applications. Currently, although much progress has been made in handling missing data, existing methods for cancer diagnosis still struggle to obtain credible predictions due to the relatively high uncertainty of missing omics data. In this paper, we propose a trusted and attention-based multi-omics integrated framework for cancer diagnosis using incomplete multi-omics data (TAIMONET). For the raw omics data, the framework first builds an attention-based shared encoder to pick out important features and align different omics data. Next, it utilizes generative adversarial learning to impute the missing omics features based on the extracted available omics features. Meanwhile, different reconstructed complete omics data are weighted and fused according to the attention mechanism. Moreover, it uses true-class-probability to calibrate the classification results and improve the reliability of the results. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed model are clearly validated.