MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network | |
Wong, L (Wong, Leon)[ 1,2,3 ]; You, ZH (You, Zhu-Hong)[ 1,2,3 ]; Guo, ZH (Guo, Zhen-Hao)[ 1,2,3 ]; Yi, HC (Yi, Hai-Cheng)[ 1,2,3 ]; Chen, ZH (Chen, Zhan-Heng)[ 1,2,3 ]; Cao, MY (Cao, Mei-Yuan)[ 4 ] | |
刊名 | ACS OMEGA
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2020 | |
卷号 | 5期号:28页码:17022-17032 |
ISSN号 | 2470-1343 |
DOI | 10.1021/acsomega.9b04195 |
英文摘要 | Analysis of miRNA-target mRNA interaction (MTI) is of crucial significance in discovering new target candidates for miRNAs. However, the biological experiments for identifying MTIs have a high false positive rate and are high-priced, time-consuming, and arduous. It is an urgent task to develop effective computational approaches to enhance the investigation of miRNA-target mRNA relationships. In this study, a novel method called MIPDH is developed for miRNA-mRNA interaction prediction by using DeepWalk on a heterogeneous network. More specifically, MIPDH extracts two kinds of features, in which a biological behavior feature is learned using a network embedding algorithm on a constructed heterogeneous network derived from 17 kinds of associations among drug, disease, and 6 kinds of biomolecules, and the attribute feature is learned using the k-mer method on sequences of miRNAs and target mRNAs. Then, a random forest classifier is trained on the features combined with the biological behavior feature and attribute feature. When implementing a 5-fold cross-validation experiment, MIPDH achieved an average accuracy, sensitivity, specificity and AUC of 75.85, 74.37, 77.33%, and 0.8044, respectively. To further evaluate the performance of MIPDH, other classifiers and feature descriptors are conducted for comparisons. MIPDH can achieve a better performance. Additionally, case studies on hsa-miR-106b-5p, hsa-let-7d-5p, and hsa-let-7e-Sp are also implemented. As a result, 14, 9, and 9 out of the top 15 targets that interacted with these miRNAs were verified using the experimental literature or other databases. All these prediction results indicate that MIPDH is an effective method for predicting miRNA-target mRNA interactions. |
WOS记录号 | WOS:000554991800004 |
内容类型 | 期刊论文 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/7394] ![]() |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
通讯作者 | You, ZH (You, Zhu-Hong)[ 1,2,3 ] |
作者单位 | 1.Guang Dong Polytech Coll, Zhaoqing 526100, Guangdong, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, XinJiang Lab Minor Speech & Language Informat Pro, Urumqi 830011, Peoples R China 4.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China |
推荐引用方式 GB/T 7714 | Wong, L ,You, ZH ,Guo, ZH ,et al. MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network[J]. ACS OMEGA,2020,5(28):17022-17032. |
APA | Wong, L ,You, ZH ,Guo, ZH ,Yi, HC ,Chen, ZH ,&Cao, MY .(2020).MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network.ACS OMEGA,5(28),17022-17032. |
MLA | Wong, L ,et al."MIPDH: A Novel Computational Model for Predicting microRNA-mRNA Interactions by DeepWalk on a Heterogeneous Network".ACS OMEGA 5.28(2020):17022-17032. |
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