School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China.
Hunan Institute of Technology, School of Computer Science and Technology, Hengyang 421002, China.
Int J Mol Sci. 2020 Feb 22;21(4):1508. doi: 10.3390/ijms21041508.
Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA-disease associations. In this research, we proposed a lncRNA-disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA-disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA-disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA-disease associations and isolated diseases.
长链非编码 RNA（lncRNA）种类繁多，参与了多种细胞发育过程和疾病，但其本身不翻译成蛋白质。通过计算方法推断与疾病相关的 lncRNA 有助于理解疾病的发病机制，但目前的计算方法仍然没有达到显著的预测性能：例如相似网络的构建不准确和已知 lncRNA-疾病关联的数量不足。在这项研究中，我们提出了一种基于整合空间投影得分的 lncRNA-疾病关联推断方法（LDAI-ISPS），该方法包括以下关键步骤：通过结合所有全局信息（例如疾病语义相似性、lncRNA 功能相似性和已知 lncRNA-疾病关联）将已知 lncRNA-疾病关联的布尔网络转换为加权网络；通过加权网络的向量投影获得空间投影得分，从而形成最终的预测得分，没有偏差。留一交叉验证（LOOCV）结果表明，与其他方法相比，LDAI-ISPS 具有更高的准确性，推断疾病的 AUC 值为 0.9154，推断新 lncRNA（其与疾病相关的关联未知）的 AUC 值为 0.8865，推断孤立疾病（其与 lncRNA 相关的关联未知）的 AUC 值为 0.7518。案例研究也证实了 LDAI-ISPS 的预测性能，它可以作为传统生物学实验的辅助工具，用于推断潜在的 lncRNA-疾病关联和孤立疾病。
