School of Information Science and Technology, University of Science and Technology of China, Hefei, AH230027, China.
Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, AH230027, China.
Sci Rep. 2018 Jan 18;8(1):1065. doi: 10.1038/s41598-018-19357-3.
Accumulating evidences have indicated that lncRNAs play an important role in various human complex diseases. However, known disease-related lncRNAs are still comparatively small in number, and experimental identification is time-consuming and labor-intensive. Therefore, developing a useful computational method for inferring potential associations between lncRNAs and diseases has become a hot topic, which can significantly help people to explore complex human diseases at the molecular level and effectively advance the quality of disease diagnostics, therapy, prognosis and prevention. In this paper, we propose a novel prediction of lncRNA-disease associations via lncRNA-disease-gene tripartite graph (TPGLDA), which integrates gene-disease associations with lncRNA-disease associations. Compared to previous studies, TPGLDA can be used to better delineate the heterogeneity of coding-non-coding genes-disease association and can effectively identify potential lncRNA-disease associations. After implementing the leave-one-out cross validation, TPGLDA achieves an AUC value of 93.9% which demonstrates its good predictive performance. Moreover, the top 5 predicted rankings of lung cancer, hepatocellular carcinoma and ovarian cancer are manually confirmed by different relevant databases and literatures, affording convincing evidence of the good performance as well as potential value of TPGLDA in identifying potential lncRNA-disease associations. Matlab and R codes of TPGLDA can be found at following: https://github.com/USTC-HIlab/TPGLDA .
越来越多的证据表明，lncRNAs 在各种人类复杂疾病中发挥着重要作用。然而，已知的与疾病相关的 lncRNAs 的数量仍然相对较少，实验鉴定既费时又费力。因此，开发一种有用的计算方法来推断 lncRNA 与疾病之间的潜在关联已成为一个热门话题，这可以帮助人们在分子水平上深入研究复杂的人类疾病，并有效提高疾病诊断、治疗、预后和预防的质量。在本文中，我们提出了一种通过 lncRNA-疾病-基因三元图（TPGLDA）预测 lncRNA-疾病关联的新方法，该方法将基因-疾病关联与 lncRNA-疾病关联相结合。与以前的研究相比，TPGLDA 可用于更好地描绘编码-非编码基因-疾病关联的异质性，并能有效识别潜在的 lncRNA-疾病关联。通过实施留一交叉验证，TPGLDA 达到了 93.9%的 AUC 值，表明其具有良好的预测性能。此外，通过不同的相关数据库和文献手动确认了肺癌、肝癌和卵巢癌的前 5 个预测排名，为 TPGLDA 识别潜在的 lncRNA-疾病关联的良好性能和潜在价值提供了令人信服的证据。TPGLDA 的 Matlab 和 R 代码可以在以下网址找到：https://github.com/USTC-HIlab/TPGLDA。
