A random forest based computational model for predicting novel lncRNA-disease associations

Autor: Peng Li, Xiaorong Zhan, Xiaojuan Zhan, Dengju Yao, Chee Keong Kwoh, Jinke Wang
Přispěvatelé: School of Computer Science and Engineering
Rok vydání: 2019
Předmět:
lncRNA-disease association prediction
Computer science
02 engineering and technology
computer.software_genre
Biochemistry
Semantic similarity
Structural Biology
Risk Factors
Neoplasms
Feature (machine learning)
Disease
lcsh:QH301-705.5
0303 health sciences
Applied Mathematics
Methodology Article
Computer Science Applications
Random forest
Area Under Curve
Bioinformatics algorithm
Feature selection
lcsh:R858-859.7
Regression Analysis
RNA
Long Noncoding
DNA microarray
Algorithms
Variable importance
0206 medical engineering
lcsh:Computer applications to medicine. Medical informatics
Machine learning
03 medical and health sciences
Humans
Computer Simulation
Molecular Biology
030304 developmental biology
Random Forest
Receiver operating characteristic
business.industry
Mechanism (biology)
Computational Biology
Variable Importance
MicroRNAs
lcsh:Biology (General)
ROC Curve
Computer science and engineering [Engineering]
Artificial intelligence
business
computer
020602 bioinformatics
Zdroj: BMC Bioinformatics
BMC Bioinformatics, Vol 21, Iss 1, Pp 1-18 (2020)
ISSN: 1471-2105
Popis: Background Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.
Databáze: OpenAIRE
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