HAMDA: Hybrid Approach for MiRNA-Disease Association prediction
文献类型：期刊论文
| 作者 | Chen, Xing1; Niu, Ya-Wei2; Wang, Guang-Hui2; Yan, Gui-Ying3
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| 刊名 | JOURNAL OF BIOMEDICAL INFORMATICS
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| 出版日期 | 2017-12-01 |
| 卷号 | 76页码:50-58 |
| 关键词 | miRNA Disease miRNA-disease association Hybrid prediction approach Recommendation systems |
| ISSN号 | 1532-0464 |
| DOI | 10.1016/j.jbi.2017.10.014 |
| 英文摘要 | For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 +/- 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. |
| 资助项目 | Fundamental Research Funds for the Central Universities[2017XKQY083] |
| WOS研究方向 | Computer Science ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:000426221400006 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/29620]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Chen, Xing; Wang, Guang-Hui |
| 作者单位 | 1.China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou 221116, Peoples R China 2.Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Xing,Niu, Ya-Wei,Wang, Guang-Hui,et al. HAMDA: Hybrid Approach for MiRNA-Disease Association prediction[J]. JOURNAL OF BIOMEDICAL INFORMATICS,2017,76:50-58. |
| APA | Chen, Xing,Niu, Ya-Wei,Wang, Guang-Hui,&Yan, Gui-Ying.(2017).HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.JOURNAL OF BIOMEDICAL INFORMATICS,76,50-58. |
| MLA | Chen, Xing,et al."HAMDA: Hybrid Approach for MiRNA-Disease Association prediction".JOURNAL OF BIOMEDICAL INFORMATICS 76(2017):50-58. |
入库方式： OAI收割
来源：数学与系统科学研究院
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