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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction
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LDAH2V: Exploring Meta-Paths Across Multiple Networks for lncRNA-Disease Association Prediction

机译：LDAH2V：探讨跨多个网络的Meta路径，用于LNCRNA-疾病协会预测

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摘要

Accumulating evidence has demonstrated dysfunctions of long non-coding RNAs (lncRNAs) are involved in various complex human diseases. However, even today, the relationships between lncRNAs and diseases remain unknown in most cases. Developing effective computational approaches to identify potential lncRNA-disease associations has become a hot topic. Existing network-based approaches are usually focused on the intrinsic features of lncRNAs and diseases but ignore the heterogeneous information of biological networks. Considering the limitations in previous methods, we propose LDAH2V, an efficient computational framework for predicting potential lncRNA-disease associations. LDAH2V uses the HIN2Vec to calculate the meta-path and feature vector for each lncRNA-disease pair in the heterogeneous information network (HIN), which consists of lncRNA similarity network, disease similarity network, miRNA similarity network, and the associations between them. Then, a Gradient Boosting Tree (GBT) classifier to predict lncRNA-disease associations is built with the feature vectors. The results show that LDAH2V performs significantly better than the four existing state-of-the-art methods and gains an AUC of 0.97 in the 10-fold cross-validation test. Furthermore, case studies of colon cancer and ovarian cancer-related lncRNAs have been confirmed in related databases and medical literature.
机译：累积证据表明了长期非编码RNA（LNCRNA）的功能障碍参与了各种复杂的人类疾病。然而，即使在今天，在大多数情况下，LNCRNA和疾病之间的关系仍然是未知的。制定有效的计算方法以确定潜在的LNCRNA疾病协会已成为一个热门话题。现有的基于网络的方法通常集中在LNCRNA和疾病的内在特征，而是忽略生物网络的异构信息。考虑到以前的方法中的局限性，我们提出了LDAH2V，一种有效的计算框架，用于预测潜在的LNCRNA疾病关联。 LDAH2V使用HIN2VEC在异构信息网络（HIN）中计算每个LNCRNA疾病对的元路径和特征载体，其由LNCRNA相似性网络，疾病相似性网络，MIRNA相似度网络和它们之间的关联组成。然后，用特征向量建立了预测LNCRNA疾病关联的梯度升压树（GBT）分类器。结果表明，LDAH2V在10倍交叉验证测试中表现出比现有的四种现有最先进的方法更好地提高0.97的AUC。此外，在相关数据库和医学文献中已经证实了对结肠癌和卵巢癌相关的LNCRNA的案例研究。

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