LAUSR

MOTIVATION Cancer subtype diagnosis is crucial for its precise treatment and different subtypes need different therapies. Although the diagnosis can be greatly improved by fusing multi-omics data, most fusion solutions depend on paired omics data, which are actually weakly-paired, with different omics views missing for different samples. Incomplete multi-view learning based solutions can alleviate this issue but are still far from satisfactory because they: (i) mainly focus on shared information while ignore the important individuality of multi-omics data; (ii) cannot pick out interpretable features for precise diagnosis. RESULTS We introduce an interpretable and flexible solution (LungDWM) for Lung cancer subtype Diagnosis using Weakly-paired Multi-omics data. LungDWM first builds an attention-based encoder for each omics to pick out important diagnostic features and extract shared and complementary information across omics. Next, it proposes an individual loss to jointly extract the specific information of each omics, and performs generative adversarial learning to impute missing omics of samples using extracted features. After that, it fuses the extracted and imputed features to diagnose cancer subtypes. Experiments on benchmark datasets show that LungDWM achieves a better performance than recent competitive methods, and has a high authenticity and good interpretability. AVAILABILITY The code is available at http://www.sdu-idea.cn/codes.php?name=LungDWM.