Background An increasing number of studies have shown that lncRNAs are crucial for the control of hormones and the regulation of various physiological processes in the human body, and deletion mutations in RNA are related to many human diseases. LncRNA- disease association prediction is very useful for understanding pathogenesis, diagnosis, and prevention of diseases, and is helpful for labelling relevant biological information. Results In this manuscript, we propose a computational model named bidirectional generative adversarial network (BiGAN), which consists of an encoder, a generator, and a discriminator to predict new lncRNA-disease associations. We construct features between lncRNA and disease pairs by utilizing the disease semantic similarity, lncRNA sequence similarity, and Gaussian interaction profile kernel similarities of lncRNAs and diseases. The BiGAN maps the latent features of similarity features to predict unverified association between lncRNAs and diseases. The computational results have proved that the BiGAN performs significantly better than other state-of-the-art approaches in cross-validation. We employed the proposed model to predict candidate lncRNAs for renal cancer and colon cancer. The results are promising. Case studies show that almost 70% of lncRNAs in the top 10 prediction lists are verified by recent biological research. Conclusion The experimental results indicated that our proposed model had an accurate predictive ability for the association of lncRNA-disease pairs.

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Identification of active candidate compounds for target proteins, also called drug–protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative ’multi-modality attributes’ learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.