IEEE J Biomed Health Inform. 2021 Mar;25(3):881-890. doi: 10.1109/JBHI.2020.2988720. Epub 2021 Mar 5.
As we all know, science and technology are developing faster and faster. Many experts and scholars have demonstrated that human diseases are related to lncRNA, but only a few associations have been confirmed, and many unknown associations need to be found. In the process of finding associations, it takes a lot of time, so finding an efficient way to predict the associations between lncRNAs and diseases is particularly important. In this paper, we propose a multi-label fusion collaborative matrix factorization (MLFCMF) approach for predicting lncRNA-disease associations (LDAs). Firstly, the lncRNA space and disease space are optimized by multi-label to enhance the intrinsic link between lncRNA and disease and to tap potential information. Multi-label learning can encode a variety of data information from the sample space. Secondly, to learn multi-label information in the data space, the fusion method is used to handle the relationship between multiple labels. More comprehensive information will be obtained by weighing the effects of different labels. The addition of Gaussian interaction profile (GIP) kernel can increase the network similarity. Finally, the lncRNA-disease associations are predicted by the method of collaborative matrix factorization. The ten-fold cross-validation method is used to evaluate the MLFCMF method, and our method finally obtains an AUC value of 0.8612. Detailed analysis of ovarian cancer, colorectal cancer, and lung cancer in the simulation experiment results. So it can be seen that our method MLFCMF is an effective model for predicting lncRNA-disease associations.
众所周知，科学技术发展得越来越快。许多专家和学者已经证明，人类疾病与 lncRNA 有关，但只有少数关联得到了证实，还有许多未知的关联需要被发现。在发现关联的过程中，需要花费大量的时间，因此找到一种有效的方法来预测 lncRNA 和疾病之间的关联尤为重要。在本文中，我们提出了一种多标签融合协同矩阵分解（MLFCMF）方法，用于预测 lncRNA-疾病关联（LDAs）。首先，通过多标签对 lncRNA 空间和疾病空间进行优化，增强 lncRNA 与疾病之间的内在联系，挖掘潜在信息。多标签学习可以从样本空间中编码各种数据信息。其次，为了在数据空间中学习多标签信息，采用融合方法处理多个标签之间的关系。通过权衡不同标签的效果，可以获得更全面的信息。加入高斯互作用图（GIP）核可以增加网络的相似性。最后，通过协同矩阵分解的方法来预测 lncRNA-疾病的关联。采用十折交叉验证方法对 MLFCMF 方法进行评估，最终得到 AUC 值为 0.8612。在模拟实验结果中对卵巢癌、结直肠癌和肺癌进行了详细分析。因此，可以看出我们的方法 MLFCMF 是一种有效的预测 lncRNA-疾病关联的模型。
