Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play essential roles in various human complex diseases. Compared with protein-coding genes, the associations between diseases and lncRNAs are still not well studied. Hence, inferring disease-associated lncRNAs on a genome-wide scale has become an urgent matter. However, known associations are still being studied in small quantities because experimental verification requires a large amount of human and material resources. To solve this problem, we proposed a method called the weight matrix of lncRNA-disease associations prediction (WLDAP) by combining various biological information. Firstly, this method incorporated information about lncRNA-disease associations. Secondly, the Gaussian interaction profile kernel was used to calculate the similarity between diseases and lncRNAs. Finally, the weighting model was used to obtain the similarity score between diseases and potential lncRNAs. After leave-one-out cross-validation (LOOCV) was applied, the AUC value of WLDAP reached 92.07%, the AUC value obtained by 5-fold cross-validation was 93.7%, indicating a relatively good prediction performance is relatively good. In addition, the top five candidates successfully predicted the rankings of colorectal cancer, lung cancer, and breast cancer. Most of these predictions are confirmed by different relevant databases and various literature, providing the value of WLDAP in demonstrating potential lncRNA-disease associations. This method will help people further explore complex human diseases at the molecular level, providing a high-quality basis for the diagnosis, prognosis, prevention, and treatment of diseases.

越来越多的证据表明，长的非编码RNA（lncRNA）在各种人类复杂疾病中起着至关重要的作用。与蛋白质编码基因相比，疾病与lncRNAs之间的关联仍未得到很好的研究。因此，在全基因组范围内推断与疾病相关的lncRNA已经成为当务之急。但是，由于实验验证需要大量的人力和物力，因此仍在少量研究已知的关联。为了解决这个问题，我们提出了一种通过结合各种生物学信息的方法，称为lncRNA-疾病关联预测权重矩阵（WLDAP）。首先，该方法结合了有关lncRNA-疾病关联的信息。其次，使用高斯相互作用谱核计算疾病与lncRNAs之间的相似性。最后，使用加权模型获得疾病和潜在的lncRNA之间的相似性评分。应用留一法交叉验证（LOOCV）后，WLDAP的AUC值达到92.07％，通过5倍交叉验证获得的AUC值为93.7％，表明相对较好的预测性能相对较好。此外，前五名候选人成功预测了结肠直肠癌，肺癌和乳腺癌的排名。这些预测大多数都由不同的相关数据库和各种文献证实，从而提供了WLDAP在证明潜在的lncRNA-疾病关联中的价值。这种方法将帮助人们在分子水平上进一步探索复杂的人类疾病，为疾病的诊断，预后，预防和治疗提供高质量的基础。加权模型被用来获得疾病和潜在的lncRNAs之间的相似性得分。应用留一法交叉验证（LOOCV）后，WLDAP的AUC值达到92.07％，通过5倍交叉验证获得的AUC值为93.7％，表明相对较好的预测性能相对较好。此外，前五名候选人成功预测了结肠直肠癌，肺癌和乳腺癌的排名。这些预测大多数都由不同的相关数据库和各种文献证实，从而提供了WLDAP在证明潜在的lncRNA-疾病关联中的价值。这种方法将帮助人们在分子水平上进一步探索复杂的人类疾病，为疾病的诊断，预后，预防和治疗提供高质量的基础。加权模型被用来获得疾病和潜在的lncRNAs之间的相似性得分。应用留一法交叉验证（LOOCV）后，WLDAP的AUC值达到92.07％，通过5倍交叉验证获得的AUC值为93.7％，表明相对较好的预测性能相对较好。此外，前五名候选人成功预测了结肠直肠癌，肺癌和乳腺癌的排名。这些预测大多数都由不同的相关数据库和各种文献证实，从而提供了WLDAP在证明潜在的lncRNA-疾病关联中的价值。这种方法将帮助人们在分子水平上进一步探索复杂的人类疾病，为疾病的诊断，预后，预防和治疗提供高质量的基础。