SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

Background: Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and diseas...

Full description

Bibliographic Details
Main Authors: Deng, L. (Author), Huang, Y. (Author), Liu, D. (Author), Nie, W. (Author), Zhang, J. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Biological experiments
biology
Breast Neoplasms
breast tumor
Computational Biology
Computational framework
Cross validation
Disease associations
Diseases
Forecasting
Functional similarity
genetics
Hepatocellular carcinoma
human
Humans
Latent feature
Learning systems
microRNA
MicroRNAs
miRNA-disease associations
RNA
Semantic similarity
Semantics
Stacked autoencoder
State-of-the-art methods
XGBoost
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items