College of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410001, China.
Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, XiangTan 411105, China.
Genes (Basel). 2019 Feb 8;10(2):126. doi: 10.3390/genes10020126.
Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we constructed three kinds of association networks, namely the lncRNA-miRNA association network, the miRNA-disease association network, and the lncRNA-disease correlation network firstly. Then, through integrating these three newly constructed association networks, we constructed an lncRNA-disease weighted association network, which would be further updated by adopting the KNN algorithm based on the semantic similarity of diseases and the similarity of lncRNA functions. Thereafter, according to the updated lncRNA-disease weighted association network, a novel computational model called PMFILDA was proposed to infer potential lncRNA-disease associations based on the probability matrix decomposition. Finally, to evaluate the superiority of the new prediction model PMFILDA, we performed Leave One Out Cross-Validation (LOOCV) based on strongly validated data filtered from MNDR and the simulation results indicated that the performance of PMFILDA was better than some state-of-the-art methods. Moreover, case studies of breast cancer, lung cancer, and colorectal cancer were implemented to further estimate the performance of PMFILDA, and simulation results illustrated that PMFILDA could achieve satisfying prediction performance as well.
最近，越来越多的研究表明，长非编码 RNA（lncRNA）可以参与各种重要的生物过程，并且可以作为治疗某些疾病（如冠状动脉疾病和各种癌症）的最有前途的生物标志物。由于成本和时间的复杂性，通过传统的生物学实验可以验证的可能与疾病相关的 lncRNA 的数量非常有限。因此，近年来，使用计算模型来预测潜在的疾病-lncRNA 关联变得非常流行。在这项研究中，我们首先构建了三种关联网络，即 lncRNA-miRNA 关联网络、miRNA-疾病关联网络和 lncRNA-疾病相关性网络。然后，通过整合这三个新构建的关联网络，我们构建了一个 lncRNA-疾病加权关联网络，该网络将通过基于疾病的语义相似性和 lncRNA 功能相似性的 KNN 算法进行更新。此后，根据更新后的 lncRNA-疾病加权关联网络，提出了一种新的计算模型 PMFILDA，用于基于概率矩阵分解来推断潜在的 lncRNA-疾病关联。最后，为了评估新预测模型 PMFILDA 的优越性，我们基于从 MNDR 过滤的强验证数据进行了留一交叉验证（LOOCV），模拟结果表明 PMFILDA 的性能优于一些最先进的方法。此外，还对乳腺癌、肺癌和结直肠癌进行了案例研究，以进一步评估 PMFILDA 的性能，模拟结果表明 PMFILDA 也可以实现令人满意的预测性能。
