RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation

2021 ◽  
Author(s):  
Guobo Xie ◽  
Bin Huang ◽  
Yuping Sun ◽  
Changhai Wu ◽  
Yuqiong Han
Keyword(s):  
Random Walk ◽  
Prediction Model ◽  
Disease Association ◽  
Label Propagation

MELPMDA: A New Method Based on Matrix Enhancement and Label Propagation for Predicting miRNA-Disease Association

2021 ◽  
pp. 536-548
Author(s):  
Zhen-Wei Zhang ◽  
Zhen Gao ◽  
Chun-Hou Zheng ◽  
Yu-Tian Wang ◽  
Su-Min Qi
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
New Method ◽  
Matrix Enhancement

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 Three-Layer Heterogeneous Network Combined With Unbalanced Random Walk for miRNA-Disease Association Prediction

2020 ◽  
Vol 10 ◽  
Author(s):  
Limin Yu ◽  
Xianjun Shen ◽  
Duo Zhong ◽  
Jincai Yang
Keyword(s):  
Random Walk ◽  
Heterogeneous Network ◽  
Disease Association

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 A Novel lncRNA-Disease Association Prediction Model Using Laplacian Regularized Least Squares and Space Projection-Federated Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 111614-111625
Author(s):  
Min Chen ◽  
Yan Peng ◽  
Ang Li ◽  
Yinwei Deng ◽  
Zejun Li
Keyword(s):  
Prediction Model ◽  
Least Squares ◽  
Disease Association ◽  
Regularized Least Squares ◽  
Space Projection ◽  
Laplacian Regularized Least Squares

scholarly journals A Novel Network-Based Computational Model for Prediction of Potential LncRNA–Disease Association

2019 ◽  
Vol 20 (7) ◽  
pp. 1549 ◽  
Author(s):  
Yang Liu ◽  
Xiang Feng ◽  
Haochen Zhao ◽  
Zhanwei Xuan ◽  
Lei Wang
Keyword(s):  
Computational Models ◽  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Weighted Matrix ◽  
Disease Associations ◽  
Resource Allocation Strategy ◽  
Simulation Results ◽  
Leave One Out ◽  
Treatment Prognosis

Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well.

LRMDA: Using Logistic Regression and Random Walk with Restart for MiRNA-Disease Association Prediction

2019 ◽  
pp. 283-293 ◽  
Author(s):  
Zhengwei Li ◽  
Ru Nie ◽  
Zhuhong You ◽  
Yan Zhao ◽  
Xin Ge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Walk ◽  
Disease Association ◽  
Random Walk With Restart
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