A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method
Autor: | Yingwei Deng, Min Chen, Yan Tan, Ang Li |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Lung Neoplasms
Computer science Disease Vectors computer.software_genre Biochemistry Field (computer science) Lung and Intrathoracic Tumors Machine Learning Mathematical and Statistical Techniques Medical Conditions Medicine and Health Sciences Gene Regulatory Networks Projection (set theory) Multidisciplinary Mathematical Models Applied Mathematics Simulation and Modeling Statistics Random walk Kidney Neoplasms Nucleic acids Identification (information) Infectious Diseases Oncology Area Under Curve Physical Sciences Medicine Anatomy Network Analysis Algorithms Network analysis Research Article Computer and Information Sciences Science Machine learning Research and Analysis Methods Cross-validation Machine Learning Algorithms Artificial Intelligence Genetics Humans Genetic Predisposition to Disease Statistical Methods Non-coding RNA Genetic Association Studies Natural antisense transcripts Receiver operating characteristic Biology and life sciences Models Genetic business.industry Cancers and Neoplasms Computational Biology Kidneys Renal System Gene regulation MicroRNAs Species Interactions Random Walk RNA Artificial intelligence Gene expression business computer Predictive modelling Mathematics Forecasting |
Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 6, p e0252971 (2021) |
ISSN: | 1932-6203 |
Popis: | A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA–disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA–disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease–miRNA association prediction model. |
Databáze: | OpenAIRE |
Externí odkaz: | |
Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |