MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations
文献类型：期刊论文
| 作者 | Liu, Yajun1; Zhang, Fan1; Ding, Yulian2; Fei, Rong1; Li, Junhuai1; Wu, Fang-Xiang3 |
| 刊名 | JOURNAL OF CELLULAR AND MOLECULAR MEDICINE
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 28期号:17页码:13 |
| 关键词 | DeepFM Laplacian regularized piRNA piRNA-disease association |
| ISSN号 | 1582-1838 |
| DOI | 10.1111/jcmm.70046 |
| 英文摘要 | PIWI-interacting RNAs (piRNAs) are a typical class of small non-coding RNAs, which are essential for gene regulation, genome stability and so on. Accumulating studies have revealed that piRNAs have significant potential as biomarkers and therapeutic targets for a variety of diseases. However current computational methods face the challenge in effectively capturing piRNA-disease associations (PDAs) from limited data. In this study, we propose a novel method, MRDPDA, for predicting PDAs based on limited data from multiple sources. Specifically, MRDPDA integrates a deep factorization machine (deepFM) model with regularizations derived from multiple yet limited datasets, utilizing separate Laplacians instead of a simple average similarity network. Moreover, a unified objective function to combine embedding loss about similarities is proposed to ensure that the embedding is suitable for the prediction task. In addition, a balanced benchmark dataset based on piRPheno is constructed and a deep autoencoder is applied for creating reliable negative set from the unlabeled dataset. Compared with three latest methods, MRDPDA achieves the best performance on the pirpheno dataset in terms of the five-fold cross validation test and independent test set, and case studies further demonstrate the effectiveness of MRDPDA. |
| 资助项目 | Young Scientists Fund of the National Natural Science Foundation of China[62202374] ; Young Scientists Fund of the National Natural Science Foundation of China[U22A2041] ; Natural Science Basic Research Program of Shaanxi Province of China[2024JC-YBMS-484] ; China Postdoctoral Science Foundation[2021 M693887] ; Natural Science and Engineering Research Council of Canada (NSERC) |
| WOS研究方向 | Cell Biology ; Research & Experimental Medicine |
| 语种 | 英语 |
| WOS记录号 | WOS:001303587200001 |
| 出版者 | WILEY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39621]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wu, Fang-Xiang |
| 作者单位 | 1.Xian Univ Technol, Sch Comp Sci & Engn, Shaanxi Key Lab Network Comp & Secur Technol, Xian, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr High Performance Comp, Shenzhen, Peoples R China 3.Univ Saskatchewan, Dept Comp Sci Biomed Engn & Mech Engn, Saskatoon, SK, Canada |
| 推荐引用方式 GB/T 7714 | Liu, Yajun,Zhang, Fan,Ding, Yulian,et al. MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations[J]. JOURNAL OF CELLULAR AND MOLECULAR MEDICINE,2024,28(17):13. |
| APA | Liu, Yajun,Zhang, Fan,Ding, Yulian,Fei, Rong,Li, Junhuai,&Wu, Fang-Xiang.(2024).MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.JOURNAL OF CELLULAR AND MOLECULAR MEDICINE,28(17),13. |
| MLA | Liu, Yajun,et al."MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations".JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 28.17(2024):13. |
入库方式： OAI收割
来源：计算技术研究所
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