Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

Wang, Li; Zhong, Cheng

doi:10.1155/2021/6652948

Cited by 6 publications

(3 citation statements)

References 68 publications

(39 reference statements)

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“…The studies of Chi et al predicted 30 miRNA features in pancreatic cancer and Li et al used RFA in predicting potential miRNAs in diabetes [ 127 , 128 ]. Multilayer perceptron was used to improve the prediction performance in the miRNA and disease associations in lymphoma (e.g., hsa-let-7a), leukemia (e.g., hsa-miR-218), kidney neoplasms (e.g., has-miR-494), colon neoplasms (e.g., has-miR-30e), and breast neoplasms (e.g., has-miR-106a) [ 129 ].…”

Section: Discussionmentioning

confidence: 99%

Streptococcus gordonii Supragingival Bacterium Oral Infection-Induced Periodontitis and Robust miRNA Expression Kinetics

Aravindraja,

Jeepipalli,

Duncan

et al. 2024

IJMS

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Streptococcus gordonii (S. gordonii, Sg) is one of the early colonizing, supragingival commensal bacterium normally associated with oral health in human dental plaque. MicroRNAs (miRNAs) play an important role in the inflammation-mediated pathways and are involved in periodontal disease (PD) pathogenesis. PD is a polymicrobial dysbiotic immune-inflammatory disease initiated by microbes in the gingival sulcus/pockets. The objective of this study is to determine the global miRNA expression kinetics in S. gordonii DL1-infected C57BL/6J mice. All mice were randomly divided into four groups (n = 10 mice/group; 5 males and 5 females). Bacterial infection was performed in mice at 8 weeks and 16 weeks, mice were euthanized, and tissues harvested for analysis. We analyzed differentially expressed (DE) miRNAs in the mandibles of S. gordonii-infected mice. Gingival colonization/infection by S. gordonii and alveolar bone resorption (ABR) was confirmed. All the S. gordonii-infected mice at two specific time points showed bacterial colonization (100%) in the gingival surface, and a significant increase in mandible and maxilla ABR (p < 0.0001). miRNA profiling revealed 191 upregulated miRNAs (miR-375, miR-34b-5p) and 22 downregulated miRNAs (miR-133, miR-1224) in the mandibles of S. gordonii-infected mice at the 8-week mark. Conversely, at 16 weeks post-infection, 10 miRNAs (miR-1902, miR-203) were upregulated and 32 miRNAs (miR-1937c, miR-720) were downregulated. Two miRNAs, miR-210 and miR-423-5p, were commonly upregulated, and miR-2135 and miR-145 were commonly downregulated in both 8- and 16-week-infected mice mandibles. Furthermore, we employed five machine learning (ML) algorithms to assess how the number of miRNA copies correlates with S. gordonii infections in mice. In the ML analyses, miR-22 and miR-30c (8-week), miR-720 and miR-339-5p (16-week), and miR-720, miR-22, and miR-339-5p (combined 8- and 16-week) emerged as the most influential miRNAs.

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Section: Discussionmentioning

confidence: 99%

Streptococcus gordonii Supragingival Bacterium Oral Infection-Induced Periodontitis and Robust miRNA Expression Kinetics

Aravindraja,

Jeepipalli,

Duncan

et al. 2024

IJMS

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“…In other words, the input is the recognized drug-target associations. The method of prediction comes in a wide range, starting from optimization to simple classical machine learning methods, e.g., random forest [10], SVM [11], and toward current state-of-the-art deep learning methods [12,13,14]. We call all those methods as "Matrix Factorization based Drug Repurposing methods" (MF-DR).…”

Section: Introductionmentioning

confidence: 99%

DRaW: An Objection on Using Matrix Factorization for Drug-Repurposing– Prediction of Covid-19 Antivirals by Deep Learning as a Case Study

Hashemi

Zabihian

Hooshmand

et al. 2022

Preprint

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Background: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have had much attention and utilization in DTIs. However, They suffer from some drawbacks. Methods: We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without input data leakage. We compare our model with several matrix factorization methods and a deep model on the Coivd-19 dataset. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Moreover, we do a docking study as external validation on the recommended drugs for Covid-19. Results: For all cases, the results confirm that DRaW outperforms the matrix factorization and deep models. The docking approves the top-ranked mode’s recommendations for Covid-19. Conclusions: In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on four benchmark datasets.

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“…PMFMDA can integrate the similarity of miRNAs and diseases and construct a probability matrix factorization algorithm by using known an association matrix and integrating a similarity matrix to deduce new miRNA–disease associations. Wang et al [ 53 ] integrated the neural network matrix factorization and multi-layer perception into the deep collaborative filtering framework to predict miRNA–disease associations. However, this method shows no enhancement in dealing with the problem on negative sample selection.…”

Section: Introductionmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA–disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA–disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease–miRNA association prediction model.

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Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

Cited by 6 publications

References 68 publications

Streptococcus gordonii Supragingival Bacterium Oral Infection-Induced Periodontitis and Robust miRNA Expression Kinetics

Streptococcus gordonii Supragingival Bacterium Oral Infection-Induced Periodontitis and Robust miRNA Expression Kinetics

DRaW: An Objection on Using Matrix Factorization for Drug-Repurposing– Prediction of Covid-19 Antivirals by Deep Learning as a Case Study

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

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