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
On-line prístup:	Získať plný text
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