Increasing number of experiments show that lncRNAs are involved in many biological processes, and their mutations and disorders are associated with many diseases. However, verifying the relationships between lncRNAs and diseases is time consuming and laborio. Searching for effective computational methods will contribute to our understanding of the underlying mechanisms of disease and identifying biomarkers of diseases. Therefore, we proposed a method called lncRNA-disease association prediction based on linear neighborhood similarity and unbalanced bi-random walk (LDA-LNSUBRW). Given that the known lncRNA-disease associations are rare, a pretreatment step should be performed to obtain the interaction possibility of unknown cases, so as to help us predict the potential associations. In the framework of leave-one-out cross-validation (LOOCV)and fivefold cross-validation (5-fold CV), LDA-LNSUBRW achieved effective performance with AUC of 0.8874 and 0.8632 $\pm$ 0.0051, respectively. The experimental results in this paper show that the proposed method is superior to five other state-of-the-art methods. In addition, case studies of three diseases (lung cancer, breast cancer, and osteosarcoma)were carried out to illustrate that LDA-LNSUBRW could predict the relevant lncRNAs.

中文翻译：

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LDA-LNSUBRW：基于线性邻域相似性和不平衡双随机游走的lncRNA-疾病关联预测

越来越多的实验表明，lncRNAs参与了许多生物过程，它们的突变和紊乱与许多疾病有关。然而，验证 lncRNA 与疾病之间的关系既费时又费力。寻找有效的计算方法将有助于我们理解疾病的潜在机制和识别疾病的生物标志物。因此，我们提出了一种基于线性邻域相似性和不平衡双随机游走（LDA-LNSUBRW）的lncRNA-疾病关联预测方法。鉴于已知的lncRNA-疾病关联很少见，应进行预处理步骤以获得未知病例的相互作用可能性，以帮助我们预测潜在的关联。$\pm$0.0051，分别。本文的实验结果表明，所提出的方法优于其他五种最先进的方法。此外，还对三种疾病（肺癌、乳腺癌和骨肉瘤）进行了案例研究，以说明 LDA-LNSUBRW 可以预测相关的 lncRNA。