SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning

•A novel hybrid computational framework for lncRNA-disease association prediction.•Improved method that combines matrix factorization and deep learning techniques.•Show good results in term of AUC and AUPR compared with the other methods. In recent years, accumulating studies have shown that long no...

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Vydané v:Methods (San Diego, Calif.) Ročník 179; s. 73 - 80
Hlavní autori: Zeng, Min, Lu, Chengqian, Zhang, Fuhao, Li, Yiming, Wu, Fang-Xiang, Li, Yaohang, Li, Min
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.07.2020
Predmet:
case studies
colorectal neoplasms
computational methodology
Deep learning
human diseases
kidney neoplasms
Linear feature
lncRNA-disease association prediction
non-coding RNA
Non-linear feature
prediction
Singular value decomposition
stomach neoplasms
Deep learning
Linear feature
lncRNA-disease association prediction
Non-linear feature
Singular value decomposition
ISSN:1046-2023, 1095-9130, 1095-9130
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Popis
Shrnutí:•A novel hybrid computational framework for lncRNA-disease association prediction.•Improved method that combines matrix factorization and deep learning techniques.•Show good results in term of AUC and AUPR compared with the other methods. In recent years, accumulating studies have shown that long non-coding RNAs (lncRNAs) not only play an important role in the regulation of various biological processes but also are the foundation for understanding mechanisms of human diseases. Due to the high cost of traditional biological experiments, the number of experimentally verified lncRNA-disease associations is very limited. Thus, many computational approaches have been proposed to discover the underlying associations between lncRNAs and diseases. However, the associations between lncRNAs and diseases are too complicated to model by using only traditional matrix factorization-based methods. In this study, we propose a hybrid computational framework (SDLDA) for the lncRNA-disease association prediction. In our computational framework, we use singular value decomposition and deep learning to extract linear and non-linear features of lncRNAs and diseases, respectively. Then we train SDLDA by combing the linear and non-linear features. Compared to previous computational methods, the combination of linear and non-linear features reinforces each other, which is better than using only either matrix factorization or deep learning. The computational results show that SDLDA has a better performance over existing methods in the leave-one-out cross-validation. Furthermore, the case studies show that 28 out of 30 cancer-related lncRNAs (10 for gastric cancer, 10 for colon cancer and 8 for renal cancer) are verified by mining recent biomedical literature. Code and data can be accessed at https://github.com/CSUBioGroup/SDLDA.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1046-2023
1095-9130
1095-9130
DOI:10.1016/j.ymeth.2020.05.002