SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

SVDNVLDA: predicting lncRNA-disease associations by Singular Value Decomposition and node2vec

Background: Numerous studies on discovering the roles of long non-coding RNAs (lncRNAs) in the occurrence, development and prognosis progresses of various human diseases have drawn substantial attentions. Since only a tiny portion of lncRNA-disease associations have been properly annotated, an incre...

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Bibliographic Details
Main Authors: Fu, K. (Author), Kong, M. (Author), Li, J. (Author), Shi, J. (Author), Wang, D. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
biology
Computational Biology
Diagnosis
Disease associations
Forecasting
genetics
human
Human disease
Humans
LncRNA-disease association prediction
long untranslated RNA
Molecular levels
Network representation
Network representation learning
node2vec
Node2vec
Nonlinear features
Predicting models
RNA, Long Noncoding
semantics
Semantics
Singular value decomposition
Singular Value Decomposition
Xgboost classifier
XGBoost classifier
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