miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Shen, Zhen; Zhang, Youhua; Han, Kyungsook; Nandi, Asoke K.; Honig, Barry; Huang, De-Shuang

doi:10.1155/2017/2498957

Cited by 82 publications

(46 citation statements)

References 86 publications

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“…MCCMF finally obtains an AUC value of 0.9563 in the five-fold cross validation. MCCMF is compared with four advanced methods such as WBNPMD [50], RLSMDA [51], GRNMF [21] and CMF [48], which proves the superior performance of our method. The ROC curves are drawn in Fig.…”

Section: Mccmf For Mirna-disease Association Predictionmentioning

confidence: 75%

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MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

Yin

Jiao

et al. 2020

Preprint

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Background: microRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile (GIP) kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix to form the GIP kernel similarity matrix. Then the Weight K Nearest Known Neighbors (WKNKN) method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization (CMF) method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the five-fold cross-validation, with an AUC of 0.9569(0.0005).Conclusions: The AUC value of MCCMF is higher than other advanced methods in the 5-fold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

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Section: Mccmf For Mirna-disease Association Predictionmentioning

confidence: 75%

“…The CMF method proposed by Shen et al that can effectively predict the potential interactions between miRNAs and diseases [48]. In this study, the idea of the CMF method is used to predict the miRNA-disease association.…”

Section: Mccmf For Mirna-disease Association Predictionmentioning

confidence: 99%

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

Yin

Jiao

et al. 2020

Preprint

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“…There are several approaches to genomic study which are commonly used to identify risk variants in common, complex diseases such Breast Cancer; GWAS (Genome-wide Association study), Candidate Gene and Familial studies. One of the most popular genetic feature inputted for study analysis are SNPs (Single Nucleotide Polymorphisms), these are variants in base pairs within the DNA sequence [8] [6]. While a majority of these SNPs will have little to no impact on the biological systems, the consequential causal sequence can lead to imbalances in chemicals, misfolds in protein polypeptide chains and instability in mRNA transcripts [9] [11][12][13][14][15].…”

Section: Background and Related Workmentioning

confidence: 99%

“…As a high penetrance mutation, the BRCA1 gene was first localised in 1990 by Hall et al [60] who utilised logarithm of the likelihood ratio for linkage, or better known as 'Lod', to ascertain a likelihood ratio ranging from 2000:1 and 1.4×10 6 :1 among the 23 tested families within the study [60]. From this, further studies were performed, leading to the discovering of the BRCA2 gene by Wooster et al [61], using similar techniques.…”

Section: Breast Cancermentioning

confidence: 99%

A Novel Approach to Detecting Epistasis using Random Sampling Regularisation

Hind¹,

Lisboa

Hussain

et al. 2020

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Epistasis is a progressive approach that complements the 'common disease, common variant' hypothesis that highlights the potential for connected networks of genetic variants collaborating to produce a phenotypic expression. Epistasis is commonly performed as a pairwise or limitless-arity capacity that considers variant networks as either variant vs variant or as high order interactions. This type of analysis extends the number of tests that were previously performed in a standard approach such as Genome-Wide Association Study (GWAS), in which False Discovery Rate (FDR) is already an issue, therefore by multiplying the number of tests up to a factorial rate also increases the issue of FDR. Further to this, epistasis introduces its own limitations of computational complexity and intensity that are generated based on the analysis performed; to consider the most intense approach, a multivariate analysis introduces a time complexity of O(n!). Proposed in this paper is a novel methodology for the detection of epistasis using interpretable methods and best practice to outline interactions through filtering processes. Using a process of Random Sampling Regularisation which randomly splits and produces sample sets to conduct a voting system to regularise the significance and reliability of biological markers, SNPs. Preliminary results are promising, outlining a concise detection of interactions. Results for the detection of epistasis, in the classification of breast cancer patients, indicated eight outlined risk candidate interactions from five variants and a singular candidate variant with high protective association.

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“…On the contrast, computeraided methods have the merits of impersonality, rapidity and repeatability [5,6]. They gain much attention in medical and biological field [7,8] for association analysis [9][10][11], feature expression [12,13] and disease detection [14,15].…”

Section: Introductionmentioning

confidence: 99%

A novel glaucomatous representation method based on Radon and wavelet transform

et al. 2019

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Background: Glaucoma is an irreversible eye disease caused by the optic nerve injury. Therefore, it usually changes the structure of the optic nerve head (ONH). Clinically, ONH assessment based on fundus image is one of the most useful way for glaucoma detection. However, the effective representation for ONH assessment is a challenging task because its structural changes result in the complex and mixed visual patterns. Method: We proposed a novel feature representation based on Radon and Wavelet transform to capture these visual patterns. Firstly, Radon transform (RT) is used to map the fundus image into Radon domain, in which the spatial radial variations of ONH are converted to a discrete signal for the description of image structural features. Secondly, the discrete wavelet transform (DWT) is utilized to capture differences and get quantitative representation. Finally, principal component analysis (PCA) and support vector machine (SVM) are used for dimensionality reduction and glaucoma detection. Results: The proposed method achieves the state-of-the-art detection performance on RIMONE-r2 dataset with the accuracy and area under the curve (AUC) at 0.861 and 0.906, respectively. Conclusion: In conclusion, we showed that the proposed method has the capacity as an effective tool for largescale glaucoma screening, and it can provide a reference for the clinical diagnosis on glaucoma.

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miRNA-Disease Association Prediction with Collaborative Matrix Factorization

Cited by 82 publications

References 86 publications

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

MCCMF: Collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

A Novel Approach to Detecting Epistasis using Random Sampling Regularisation

A novel glaucomatous representation method based on Radon and wavelet transform

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