LPI-SKF: Predicting lncRNA-Protein Interactions Using Similarity Kernel Fusions - Zhou, Yuan-Ke; Hu, Jie; Shen, Zi-Ang; Zhang, Wen-Ya; Du, Pu-Feng (Frontiers in Genetics, 2020)
LncRNAs (Long non-coding RNA) play an important role in serval biological activities, including transcription, splicing, translation, and some other cellular regulation processes. LncRNAs perform their biological functions by interacting with various proteins. The studies on lncRNA-protein interactions are of great value to the understanding of lncRNA functional mechanisms. In this paper, we propose a novel model to predict potential lncRNA-protein interactions using the SKF (Similarity Kernel Fusion) and LapRLS (Laplacian Regularized Least Squares) algorithms. We name this method as the LPI-SKF. Various similarities of both lncRNAs and proteins were integrated in the LPI-SKF. LPI-SKF can be applied in predicting potential interactions involving novel proteins or lncRNAs. We obtained an AUROC (Area Under Receiver Operating Curve) of 0.909 in a 5-fold cross-validation, which outperforms state-of-the-art methods. 19 out of top 20 ranked interaction predictions had been verified by existing data, which implied that the LPI-SKF has a great potential in discovering unknown lncRNA-protein interactions accurately. All data and codes of this work can be downloaded from a GitHub repository (https://github.com/zyk2118216069/LPI-SKF).