iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

2019 ◽  
Vol 21 (4) ◽  
pp. 1356-1367 ◽  
Author(s):  
Hang Wei ◽  
Bin Liu
Keyword(s):  
Matrix Factorization ◽  
Complex Disease ◽  
False Negative ◽  
Computational Method ◽  
Circular Rnas ◽  
Disease Mechanism ◽  
Disease Similarity ◽  
The Matrix ◽  
Disease Associations ◽  
Non Coding Rnas

Abstract Circular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

Prediction of lncRNA-disease associations based on robust multi-label learning

2021 ◽  
Vol 16 ◽  
Author(s):  
Jiaxin Zhang ◽  
Quanmeng Sun ◽  
Cheng Liang
Keyword(s):  
Complex Disease ◽  
Cross Validation ◽  
Disease Diagnosis ◽  
Vital Role ◽  
Data Sets ◽  
Disease Similarity ◽  
Disease Associations ◽  
Comparable Performance ◽  
Non Coding Rnas ◽  
Similarity Matrices

Background: Long non-coding RNAs (lncRNAs) are nonprotein-coding transcripts of more than 200 nucleotides in length. In recent years, studies have shown that long non-coding RNAs (lncRNA) play a vital role in various biological processes, complex disease diagnosis, prognosis, and treatment. Objective: Analysis of known lncRNA-disease associations and the prediction of potential lncRNA-disease associations are necessary to provide the most probable candidates for subsequent experimental validation. Methods: In this paper, we present a novel robust computational framework for lncRNA-disease association prediction by combining the ℓ1-norm graph with multi-label learning. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases using known associations. Then, both lncRNA and disease similarity matrices are adaptively re-weighted to enhance the robustness via the ℓ1-norm graph. Lastly, the association matrix is updated with a graph-based multi-label learning framework to uncover the underlying consistency between the lncRNA space and the disease space. Results : We compared the proposed method with the four latest methods on five widely used data sets. The experimental results show that our method can achieve comparable performance in both five-fold cross-validation and leave-one-disease-out cross-validation prediction tasks. The case study of prostate cancer further confirms the practicability of our approach in identifying lncRNAs as potential prognostic biomarkers. Conclusion: Our method can serve as a useful tool for the prediction of novel lncRNA-disease associations.

scholarly journals GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations

2021 ◽  
Vol 22 (16) ◽  
pp. 8505
Author(s):  
Cunmei Ji ◽  
Zhihao Liu ◽  
Yutian Wang ◽  
Jiancheng Ni ◽  
Chunhou Zheng
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
State Of The Art ◽  
Computational Method ◽  
Circular Rnas ◽  
Attention Network ◽  
Initial Node ◽  
New Class ◽  
Disease Associations ◽  
Non Coding Rnas

Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA–disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA–disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA–disease associations.

scholarly journals NEDD: a network embedding based method for predicting drug-disease associations

2020 ◽  
Vol 21 (S13) ◽  
Author(s):  
Renyi Zhou ◽  
Zhangli Lu ◽  
Huimin Luo ◽  
Ju Xiang ◽  
Min Zeng ◽  
...  
Keyword(s):  
Drug Repositioning ◽  
Computational Method ◽  
Heterogeneous Information ◽  
Gold Standard Dataset ◽  
Disease Similarity ◽  
Disease Associations ◽  
Meta Path ◽  
Approved Drugs ◽  
Low Dimensional ◽  
Novel Associations

Abstract Background Drug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases. Results In this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases. Conclusions The experiments on a gold standard dataset which contains 1933 validated drug–disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.

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 EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments

2020 ◽  
Vol 49 (D1) ◽  
pp. D86-D91
Author(s):  
Bailing Zhou ◽  
Baohua Ji ◽  
Kui Liu ◽  
Guodong Hu ◽  
Fei Wang ◽  
...  
Keyword(s):  
Interaction Network ◽  
Circular Rnas ◽  
Biological Processes ◽  
Drug Resistant ◽  
Functional Roles ◽  
Visual Interaction ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Central Database ◽  
Data Browsing

Abstract Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

scholarly journals Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Feng Zhou ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Zhen Cui ◽  
Jing-Xiu Zhao ◽  
...  
Keyword(s):  
Bipartite Graph ◽  
Matrix Factorization ◽  
Cross Validation ◽  
Rapid Development ◽  
Factorization Method ◽  
Computational Method ◽  
Human Diseases ◽  
Simulation Experiments ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

scholarly journals GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2595
Author(s):  
Chen Bian ◽  
Xiu-Juan Lei ◽  
Fang-Xiang Wu
Keyword(s):  
Computational Method ◽  
Circular Rnas ◽  
Attention Network ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Circular Structure ◽  
Disease Associations ◽  
Network Similarity ◽  
Auc Value ◽  
Fold Cross Validation

CircRNAs (circular RNAs) are a class of non-coding RNA molecules with a closed circular structure. CircRNAs are closely related to the occurrence and development of diseases. Due to the time-consuming nature of biological experiments, computational methods have become a better way to predict the interactions between circRNAs and diseases. In this study, we developed a novel computational method called GATCDA utilizing a graph attention network (GAT) to predict circRNA–disease associations with disease symptom similarity, network similarity, and information entropy similarity for both circRNAs and diseases. GAT learns representations for nodes on a graph by an attention mechanism, which assigns different weights to different nodes in a neighborhood. Considering that the circRNA–miRNA–mRNA axis plays an important role in the generation and development of diseases, circRNA–miRNA interactions and disease–mRNA interactions were adopted to construct features, in which mRNAs were related to 88% of miRNAs. As demonstrated by five-fold cross-validation, GATCDA yielded an AUC value of 0.9011. In addition, case studies showed that GATCDA can predict unknown circRNA–disease associations. In conclusion, GATCDA is a useful method for exploring associations between circRNAs and diseases.

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 Annotation and curation of the causality information in LncRNADisease

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Kaiwen Jia ◽  
Yuanxu Gao ◽  
Jiangcheng Shi ◽  
Yuan Zhou ◽  
Yong Zhou ◽  
...  
Keyword(s):  
Circular Rnas ◽  
Non Coding Rna ◽  
Disease Associations ◽  
Non Coding Rnas ◽  
Long Non Coding Rna ◽  
Causality Information

Abstract Disease causative non-coding RNAs (ncRNAs) are of great importance in understanding a disease, for they directly contribute to the development or progress of a disease. Identifying the causative ncRNAs can provide vital implications for biomedical researches. In this work, we updated the long non-coding RNA disease database (LncRNADisease) with long non-coding RNA (lncRNA) causality information with manual annotations of the causal associations between lncRNAs/circular RNAs (circRNAs) and diseases by reviewing related publications. Of the total 11 568 experimental associations, 2297 out of 10 564 lncRNA-disease associations and 198 out of 1004 circRNA-disease associations were identified to be causal, whereas 635 lncRNAs and 126 circRNAs were identified to be causative for the development or progress of at least one disease. The updated information and functions of the database can offer great help to future researches involving lncRNA/circRNA-disease relationship. The latest LncRNADisease database is available at http://www.rnanut.net/lncrnadisease.

scholarly journals Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wen Dai ◽  
Xi Liu ◽  
Yibo Gao ◽  
Lin Chen ◽  
Jianglong Song ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
New Drugs ◽  
Biological Information ◽  
Gene Interactions ◽  
Computational Techniques ◽  
Interaction Data ◽  
Factorization Model ◽  
The Matrix ◽  
Disease Associations ◽  
Novel Drug

There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs.

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