scholarly journals Integrating random walk and binary regression to identify novel miRNA-disease association

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ya-Wei Niu ◽  
Guang-Hui Wang ◽  
Gui-Ying Yan ◽  
Xing Chen
Keyword(s):  
Random Walk ◽  
Disease Association ◽  
Binary Regression ◽  
Novel Mirna

scholarly journals miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhen Shen ◽  
You-Hua Zhang ◽  
Kyungsook Han ◽  
Asoke K. Nandi ◽  
Barry Honig ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Noncoding Rna ◽  
Esophageal Neoplasms ◽  
Kidney Neoplasms ◽  
Disease Association ◽  
Computational Method ◽  
Experimental Identification ◽  
Novel Mirna ◽  
Disease Associations ◽  
High Prediction

As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

scholarly journals IIRWR: Internal Inclined Random Walk With Restart for LncRNA-Disease Association Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 54034-54041 ◽  
Author(s):  
Lei Wang ◽  
Yubin Xiao ◽  
Jiechen Li ◽  
Xiang Feng ◽  
Qian Li ◽  
...  
Keyword(s):  
Random Walk ◽  
Disease Association ◽  
Random Walk With Restart

scholarly journals Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia Qu ◽  
Chun-Chun Wang ◽  
Shu-Bin Cai ◽  
Wen-Di Zhao ◽  
Xiao-Long Cheng ◽  
...  
Keyword(s):  
Random Walk ◽  
Heterogeneous Networks ◽  
Disease Association ◽  
Random Walk With Restart ◽  
Candidate Mirnas ◽  
Biased Random Walk ◽  
Disease Associations ◽  
And Performance ◽  
Function Similarity ◽  
Leave One Out

Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.

scholarly journals Benchmark of computational methods for predicting microRNA-disease associations

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhou Huang ◽  
Leibo Liu ◽  
Yuanxu Gao ◽  
Jiangcheng Shi ◽  
Qinghua Cui ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Disease Association ◽  
Prediction Methods ◽  
Similarity Matrix ◽  
Future Directions ◽  
Novel Mirna ◽  
Disease Associations ◽  
Precision Recall Curve ◽  
Single Predictor ◽  
Recall Curve

Abstract Background A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness. Results Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations. Conclusion Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.

Sign in / Sign up

Export Citation Format

Share Document