MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations

doi:10.1111/jcmm.70046

	MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations
	Liu, Yajun 1; Zhang, Fan 1; Ding, Yulian 2; Fei, Rong 1; Li, Junhuai 1; Wu, Fang-Xiang 3
	2024-09-01
发表期刊	JOURNAL OF CELLULAR AND MOLECULAR MEDICINE
ISSN	1582-1838
卷号	28 期号:17 页码:13
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.
关键词	DeepFM Laplacian regularized piRNA piRNA-disease association
DOI	10.1111/jcmm.70046
收录类别	SCI
语种	英语
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
WOS类目	Cell Biology ; Medicine, Research & Experimental
WOS记录号	WOS:001303587200001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39622
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.