Predicting human microRNA-disease associations based on support vector machine

Jiang, Qinghua; Wang, Guohua; Jin, Shuilin; Yu, Li; Wang, Yadong

doi:10.1504/ijdmb.2013.056078

Cited by 220 publications

(114 citation statements)

References 29 publications

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“…Another well-known example is that mir-375 can regulate insulin secretion4344. Therefore, identifying disease-related miRNAs is one of the most important goals of biomedical research, which can benefit the understanding of disease pathogenesis at the molecular level, molecular tools design for disease diagnosis, treatment and prevention31323334364546. Searching for disease-miRNA associations form experimental methods is expensive and time-consuming4546.…”

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Semi-supervised learning for potential human microRNA-disease associations inference

Chen

2014

Sci Rep

325

290

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MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.

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“…Jiang, et al46 and Xu, et al40 extracted different feature vectors and developed the support vector machine classifier to distinguish positive disease miRNAs from negative ones, respectively. As we all known, selecting negative disease-related miRNAs is currently difficult or even impossible.…”

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confidence: 99%

Semi-supervised learning for potential human microRNA-disease associations inference

Chen

2014

Sci Rep

325

290

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“…Supervised machine learning approaches for bioinformatics problems have been widely used (Liu et al, 2012;Chen, 2008;Wang and Wu, 2006;Yu et al, 2013;Erdoğdu et al, 2013;Jiang et al, 2013;Rider et al, 2014;Huang, 2013). The problem of identifying splice sites using machine learning techniques has also been addressed, mostly by supervised methods (Baten, et al, 2006;Baten et al, 2007;Sonnenburg et al, 2007;Castelo and Guigó, 2004;Batuwita and Palade, 2012).…”

Section: Related Workmentioning

confidence: 99%

An empirical study of self-training and data balancing techniques for splice site prediction

Stănescu

Caragea

2017

IJBRA

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Thanks to Next Generation Sequencing technologies, unlabelled data is now generated easily, while the annotation process remains expensive. Semi-supervised learning represents a cost-effective alternative to supervised learning, as it can improve supervised classifiers by making use of unlabelled data. However, semi-supervised learning has not been studied much for problems with highly skewed class distributions, which are prevalent in bioinformatics. To address this limitation, we carry out a study of a semi-supervised learning algorithm, specifically self-training based on Naïve Bayes, with focus on data-level approaches for handling imbalanced class distributions. Our study is conducted on the problem of predicting splice sites and it is based on datasets for which the ratio of positive to negative examples is 1-to-99. Our results show that under certain conditions semi-supervised learning algorithms are a better choice than purely supervised classification algorithms.

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“…Jiang et al adopted the support vector machine (SVM) to predict the associations between miRNAs and diseases. They first extracted a set of features for each positive and negative miRNA-disease association, and then trained the SVM classifier with the constructed features to classify candidate disease-related miRNAs[23]. Chen et al developed RBMMMDA which can not only predict the new associations between miRNAs and diseases, but also obtain the type of corresponding association[24].…”

Section: Introductionmentioning

confidence: 99%

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Liang

Xiao

et al. 2018

RNA Biology

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Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

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Predicting human microRNA-disease associations based on support vector machine

Cited by 220 publications

References 29 publications

Semi-supervised learning for potential human microRNA-disease associations inference

Semi-supervised learning for potential human microRNA-disease associations inference

An empirical study of self-training and data balancing techniques for splice site prediction

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

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