Liu Bailong, Zhu Xiaoyan, Zhang Lei, Liang Zhizheng, Li Zhengwei
Engineering Research Center of Mine Digitalization of Ministry of Education, China University of Mining and Technology, Xuzhou, China.
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
BMC Bioinformatics. 2021 Mar 25;22(1):161. doi: 10.1186/s12859-021-04092-w.
Cumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results.
We present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in this article. The combined embedding information of miRNA and disease is composed of pair embedding and node embedding. Compared with the previous heterogeneous network methods that are merely node-centric to simply compute the similarity of miRNA and disease, our method fuses pair embedding to pay more attention to capturing the features behind the relative information, which models the fine-grained pairwise relationship better than the previous case when each node only has a single embedding. First, we construct the heterogeneous network from supported miRNA-disease pairs, disease semantic similarity and miRNA functional similarity. Given by the above heterogeneous network, we find all the associated context paths of each confirmed miRNA and disease. Meta-paths are linked by nodes and then input to the gate recurrent unit (GRU) to directly learn more accurate similarity measures between miRNA and disease. Here, the multi-head attention mechanism is used to weight the hidden state of each meta-path, and the similarity information transmission mechanism in a meta-path of miRNA and disease is obtained through multiple network layers. Second, pair embedding of miRNA and disease is fed to the multi-layer perceptron (MLP), which focuses on more important segments in pairwise relationship. Finally, we combine meta-path based node embedding and pair embedding with the cost function to learn and predict miRNA-disease association. The source code and data sets that verify the results of our research are shown at https://github.com/liubailong/CEMDA .
The performance of CEMDA in the leave-one-out cross validation and fivefold cross validation are 93.16% and 92.03%, respectively. It denotes that compared with other methods, CEMDA accomplishes superior performance. Three cases with lung cancers, breast cancers, prostate cancers and pancreatic cancers show that 48,50,50 and 50 out of the top 50 miRNAs, which are confirmed in HDMM V2.0. Thus, this further identifies the feasibility and effectiveness of our method.
生物学实验的累积证据已证实，微小RNA（miRNA）在复杂疾病的诊断和治疗中具有重要作用。然而，传统医学实验存在耗时且成本高的局限性，以至于未能发现未确认的miRNA与疾病的相互作用。因此，发现潜在的miRNA - 疾病关联将有助于降低疾病的发病机制并有益于疾病治疗。尽管现有使用不同计算算法的方法在寻找潜在的miRNA - 疾病相互作用方面具有良好的性能，但我们仍需要做一些工作来改进实验结果。
在本文中，我们提出了一种用于预测miRNA - 疾病关联的新型组合嵌入模型（CEMDA）。miRNA和疾病的组合嵌入信息由对嵌入和节点嵌入组成。与以往仅以节点为中心简单计算miRNA和疾病相似度的异质网络方法相比，我们的方法融合了对嵌入，更注重捕捉相对信息背后的特征，当每个节点只有单个嵌入时，它比以前的情况能更好地对细粒度的成对关系进行建模。首先，我们从支持的miRNA - 疾病对、疾病语义相似度和miRNA功能相似度构建异质网络。基于上述异质网络，我们找到每个已确认的miRNA和疾病的所有相关上下文路径。元路径由节点链接，然后输入到门控循环单元（GRU）以直接学习miRNA和疾病之间更准确的相似度度量。这里，多头注意力机制用于对每个元路径的隐藏状态进行加权，通过多个网络层获得miRNA和疾病的元路径中的相似度信息传递机制。其次，将miRNA和疾病的对嵌入输入到多层感知器（MLP），该感知器专注于成对关系中更重要的部分。最后，我们将基于元路径的节点嵌入和对嵌入与代价函数相结合，以学习和预测miRNA - 疾病关联。验证我们研究结果的源代码和数据集显示在https://github.com/liubailong/CEMDA 。
CEMDA在留一法交叉验证和五折交叉验证中的性能分别为93.16%和92.03%。这表明与其他方法相比，CEMDA具有卓越的性能。针对肺癌、乳腺癌、前列腺癌和胰腺癌的三个案例表明，在HDMM V2.0中确认的前50个miRNA中，分别有48、50、50和50个。因此，这进一步确定了我们方法的可行性和有效性。
