The following study was conducted by Scientists from Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China; University of the Chinese Academy of Sciences, Beijing, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China; School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China; Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China; University of the Chinese Academy of Sciences, Beijing, China; Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, China. Study is published in iScience Journal – Cell Press Publishing as detailed below.
iScience Journal – Cell Press Publishing (2021)
DANE-MDA: Predicting microRNA-Disease Associations via Deep Attributed Network Embedding
Highlights
- A computational machine learning-based method for miRNA-disease association prediction
- Preserve structure and attribute features via deep attributed network embedding
- Capture the interaction between two kinds of features from diverse degrees of proximity
- Extract the higher-order features via deep stacked auto-encoder neural network
Summary
Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database.
Source:
iScience Journal – Cell Press Publishing
URL: https://www.cell.com/iscience/fulltext/S2589-0042(21)00423-5
Citation:
Ji, B.-Y., You, Z.-H., Wang, Y., Li, Z.-W., Wong, L., 2021. DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding. iScience 24(6).
