An improved computational method for prediction of lncRNA-disease associations based on collaborative filtering and resource allocation

Abstract Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

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Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Current Gene Therapy ◽

10.2174/1566523219666190917155959 ◽

2019 ◽

Vol 19 (4) ◽

pp. 232-241 ◽

Cited By ~ 5

Author(s):

Xuegong Chen ◽

Wanwan Shi ◽

Lei Deng

Keyword(s):

Protein Interactions ◽

Experimental Studies ◽

Treatment Strategies ◽

Computational Method ◽

Biological Information ◽

Support Vector ◽

Protein Protein Interactions ◽

Efficient Treatment ◽

Disease Associations ◽

Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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

BMC Bioinformatics ◽

10.1186/s12859-020-03682-4 ◽

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.

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miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Complexity ◽

10.1155/2017/2498957 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 29

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.

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Network Analysis of Neurodegenerative Disease Highlights a Role of Toll-Like Receptor Signaling

BioMed Research International ◽

10.1155/2014/686505 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 5

Author(s):

Thanh-Phuong Nguyen ◽

Laura Caberlotto ◽

Melissa J. Morine ◽

Corrado Priami

Keyword(s):

Molecular Mechanisms ◽

Interaction Network ◽

Computational Method ◽

Molecular Networks ◽

Potential Candidate ◽

Receptor Signaling ◽

Toll Like Receptor ◽

Ppi Network ◽

Disease Associations ◽

Insight Into

Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computational method based on protein-protein interaction network (PPI) to model the functional network of NDs. The aim of this work is fourfold: (i) reconstruction of a PPI network relating to the NDs, (ii) construction of an association network between diseases based on proximity in the disease PPI network, (iii) quantification of disease associations, and (iv) inference of potential molecular mechanism involved in the diseases. The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration.

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PMDFI: Predicting miRNA–Disease Associations Based on High-Order Feature Interaction

Frontiers in Genetics ◽

10.3389/fgene.2021.656107 ◽

2021 ◽

Vol 12 ◽

Author(s):

Mingyan Tang ◽

Chenzhe Liu ◽

Dayun Liu ◽

Junyi Liu ◽

Jiaqi Liu ◽

...

Keyword(s):

Computational Models ◽

Treatment Method ◽

High Order ◽

Computational Method ◽

Feature Interaction ◽

Rna Molecules ◽

Feature Interactions ◽

Non Coding Rna ◽

Disease Associations ◽

Fold Cross Validation

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

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fuNTRp: Identifying protein positions for variation driven functional tuning

10.1101/578757 ◽

2019 ◽

Cited By ~ 3

Author(s):

Maximilian Miller ◽

Daniel Vitale ◽

Peter Kahn ◽

Burkhard Rost ◽

Yana Bromberg

Keyword(s):

Protein Design ◽

Computational Method ◽

Protein Activity ◽

Protein Functions ◽

Disease Associations ◽

Position Type ◽

Variant Effect ◽

Evolution Understanding ◽

The Impact

ABSTRACTEvaluating the impact of non-synonymous genetic variants is essential for uncovering disease associations and mechanisms of evolution. Understanding corresponding sequence changes is also fundamental for synthetic protein design and stability assessments. However, the performance gain of variant effect predictors observed in recent years is not in line with the increased complexity of new methods. One likely reason for this might be that most approaches use similar sets of gene/protein features for modeling variant effect, often emphasizing sequence conservation. While high levels of conservation highlight residues essential for protein activity, much of the in vivo observable variation is arguably weaker in its impact and, thus, requires evaluation at a higher level of resolution. Here we describe function Neutral/Toggle/Rheostat predictor (funtrp), a novel computational method that categorizes protein positions based on the position-specific expected range of mutational impacts: Neutral (weak/no effects), Rheostat (function-tuning positions), or Toggle (on/off switches). We show that position types do not correlate strongly with familiar protein features such as conservation or protein disorder. We also find that position type distribution varies across different protein functions. Finally, we demonstrate that position types reflect experimentally determined functional effects and can thus improve performance of existing variant effect predictors and suggest a way forward for the development of new ones.

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

BMC Bioinformatics ◽

10.1186/s12859-021-04486-w ◽

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.

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MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning

Frontiers in Genetics ◽

10.3389/fgene.2021.657182 ◽

2021 ◽

Vol 12 ◽

Author(s):

Bo-Wei Zhao ◽

Zhu-Hong You ◽

Leon Wong ◽

Ping Zhang ◽

Hao-Yuan Li ◽

...

Keyword(s):

High Efficiency ◽

Drug Repositioning ◽

Representation Learning ◽

Computational Method ◽

Graph Representation ◽

Practical Applications ◽

Study Results ◽

Disease Associations ◽

Better Than

Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.

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Deep-belief network for predicting potential miRNA-disease associations

Briefings in Bioinformatics ◽

10.1093/bib/bbaa186 ◽

2020 ◽

Author(s):

Xing Chen ◽

Tian-Hao Li ◽

Yan Zhao ◽

Chun-Chun Wang ◽

Chi-Chi Zhu

Keyword(s):

Computational Models ◽

Cross Validation ◽

Biological Data ◽

Deep Belief Network ◽

Computational Method ◽

Restricted Boltzmann Machines ◽

Belief Network ◽

Disease Associations ◽

Leave One Out ◽

The Impact

Abstract MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.

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