NCPHLDA: a novel method for human lncRNA–disease association prediction based on network consistency projection

2019 ◽  
Vol 15 (6) ◽  
pp. 442-450 ◽  
Author(s):  
Guobo Xie ◽  
Zecheng Huang ◽  
Zhenguo Liu ◽  
Zhiyi Lin ◽  
Lei Ma
Keyword(s):  
Disease Association ◽  
Human Diseases ◽  
Novel Method ◽  
Network Consistency

In recent years, an increasing number of biological experiments and clinical reports have shown that lncRNA is closely related to the development of various complex human diseases.

scholarly journals A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

2019 ◽  
Vol 16 (2) ◽  
pp. 688-693 ◽  
Author(s):  
Pengyao Ping ◽  
Lei Wang ◽  
Linai Kuang ◽  
Songtao Ye ◽  
Muhammad Faisal Buland Iqbal ◽  
...  
Keyword(s):  
Disease Association ◽  
Association Network ◽  
Novel Method

scholarly journals NCPCDA: network consistency projection for circRNA–disease association prediction

RSC Advances ◽  
2019 ◽  
Vol 9 (57) ◽  
pp. 33222-33228 ◽  
Author(s):  
Guanghui Li ◽  
Yingjie Yue ◽  
Cheng Liang ◽  
Qiu Xiao ◽  
Pingjian Ding ◽  
...  
Keyword(s):  
Disease Association ◽  
Projection Model ◽  
Network Consistency

A network consistency projection model for predicting novel circRNA–disease interactions.

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.

A novel method for identifying potential disease-related miRNAs via a disease–miRNA–target heterogeneous network

2017 ◽  
Vol 13 (11) ◽  
pp. 2328-2337 ◽  
Author(s):  
Liang Ding ◽  
Minghui Wang ◽  
Dongdong Sun ◽  
Ao Li
Keyword(s):  
Heterogeneous Network ◽  
Mirna Target ◽  
Human Diseases ◽  
Non Coding Rna ◽  
Novel Method

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases.

scholarly journals NEMPD: A network embedding-based miRNA-protein-disease network method for the miRNA-disease association prediction

2020 ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Zhan-Heng Chen ◽  
Leon Wong ◽  
Hai-Cheng Yi
Keyword(s):  
Structural Information ◽  
Disease Association ◽  
Human Diseases ◽  
Single Type ◽  
Mirna Sequence ◽  
Sequence Information ◽  
Network Embedding ◽  
Disease Network ◽  
Graph Representations ◽  
Disease Associations

Abstract Background As an important non-coding RNA newly discovered in recent years, MicroRNA (miRNA) plays an important role in a series of life processes and is closely associated with a variety of human diseases. Hence, the identification of potential miRNA-disease associations can make great contributions to the research and treatment of human diseases. However, to our knowledge, many of the existing state-of-the-art computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results In this paper, a network embedding-based the tripartite miRNA-protein-disease network (NEMPD) method was proposed for the prediction of miRNA-disease associations. Firstly, a tripartite miRNA-protein-disease network is created by integrating known miRNA-protein and protein-disease associations. Then, we utilize the network representation method-Learning Graph Representations with Global Structural Information (GraRep) to obtain the behavior information (associations with proteins in the network) of miRNAs and diseases. Secondly, the behavior information of miRNAs and diseases is combined with the attribute information of them (disease semantic similarity and miRNA sequence information) to represent miRNA-disease pairs. Thirdly, the prediction model was established based on these known miRNA-disease pairs and the Random Forest algorithm. In the results, under five-fold cross validation, the average prediction accuracy, sensitivity, and AUC of NEMPD is 85.41%, 80.96%, and 91.58%. Furthermore, the performance of NEMPD was also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. Conclusions NEMPD has a good performance in predicting the potential associations between miRNAs and diseases and has great potency in the field of miRNA-disease association prediction in the future.

scholarly journals ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-Tian Wang ◽  
Lei Li ◽  
Cun-Mei Ji ◽  
Chun-Hou Zheng ◽  
Jian-Cheng Ni
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
Human Diseases ◽  
Biological Information ◽  
Disease Similarity ◽  
Kernel Fusion ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Similarity Networks ◽  
Association Data

MicroRNAs (miRNAs) are small non-coding RNAs that have been demonstrated to be related to numerous complex human diseases. Considerable studies have suggested that miRNAs affect many complicated bioprocesses. Hence, the investigation of disease-related miRNAs by utilizing computational methods is warranted. In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs. First, we utilized similarity kernel fusion to integrate different types of biological information for generating miRNA and disease similarity networks. Second, we applied the weighted k-nearest known neighbor algorithm to update verified miRNA–disease association data. Third, we utilized improved label propagation in disease and miRNA similarity networks to make association prediction. Furthermore, we obtained final prediction scores by adopting an average ensemble method to integrate the two kinds of prediction results. To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness of ILPMDA. All results demonstrated that ILPMDA had the ability to discover potential miRNA–disease associations.

scholarly journals Potential circRNA-disease association prediction using DeepWalk and network consistency projection

2020 ◽  
Vol 112 ◽  
pp. 103624
Author(s):  
Guanghui Li ◽  
Jiawei Luo ◽  
Diancheng Wang ◽  
Cheng Liang ◽  
Qiu Xiao ◽  
...  
Keyword(s):  
Disease Association ◽  
Network Consistency

scholarly journals LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

RSC Advances ◽  
2019 ◽  
Vol 9 (51) ◽  
pp. 29747-29759 ◽  
Author(s):  
Yi Zhang ◽  
Min Chen ◽  
Xiaohui Cheng ◽  
Zheng Chen
Keyword(s):  
Prediction Model ◽  
Disease Association ◽  
Human Diseases ◽  
Pathogenic Mechanisms ◽  
Novel Mirna ◽  
Research Findings ◽  
Space Projection ◽  
Laplacian Score

Lots of research findings have indicated that the mutations and disorders of miRNAs (microRNAs) are closely related to diseases. Therefore, determining the associations between human diseases and miRNAs is key to understand the pathogenic mechanisms.

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