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
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