IEEE/ACM Trans Comput Biol Bioinform. 2019 Mar-Apr;16(2):688-693. doi: 10.1109/TCBB.2018.2827373. Epub 2018 Apr 16.
An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA-disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leave-one-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004, and 0.9292 for known lncRNA-disease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.
越来越多的研究表明，长非编码 RNA（lncRNA）在许多重要的生物学过程中发挥着关键作用。预测潜在的 lncRNA-疾病关联可以帮助我们更好地理解人类疾病的分子机制，并有助于找到疾病诊断、治疗和预防的生物标志物。在本文中，我们构建了一个基于已知 lncRNA-疾病关联的二分网络；在此基础上，我们提出了一种新的模型来推断潜在的 lncRNA-疾病关联。具体来说，我们分析了二分网络的性质，发现它非常符合幂律分布。此外，为了评估我们模型的性能，我们实现了一个留一交叉验证（LOOCV）框架，模拟结果表明，我们的计算模型显著优于之前的最先进模型，在 LncRNADisease 数据库、Lnc2Cancer 数据库和 MNDR 数据库中分别获得的已知 lncRNA-疾病关联的 AUC 值分别为 0.8825、0.9004 和 0.9292。因此，我们的方法可能在未来成为生物医学研究领域的一个很好的补充。
