The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions

Zhang, Wen; Qu, Qianlong; Zhang, Yunqiu; Wang, Wei

doi:10.1016/j.neucom.2017.07.065

Cited by 144 publications

(97 citation statements)

References 33 publications

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“…In the future, we will further consider deep learning-based models to better integrate diverse biological data and improve predictive performances. Finally, the linear neighborhood propagation method (Zhang et al, 2018a(Zhang et al, , 2019c) may be efficiently applied to SMiR association prediction.…”

Section: Conclusion and Further Researchmentioning

confidence: 99%

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

Liu

Peng

Tian

et al. 2020

Front. Bioeng. Biotechnol.

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Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.

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Section: Conclusion and Further Researchmentioning

confidence: 99%

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

Liu

Peng

Tian

et al. 2020

Front. Bioeng. Biotechnol.

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show abstract

“…Therefore, the first step of our algorithm is to construct the feature vectors for both diseases and miRNAs. As presented in the previous work[64], we adopted ‘interaction profile’ to build the features for miRNAs and diseases. Specifically, suppose the miRNA-disease interaction network consists of m RNAs and n diseases, where ( M 1 , M 2 , M 3 , …, M m ) and ( D 1 , D 2 , D 3 , …, D n ) represent the miRNA set and disease set, respectively.…”

Section: Glnmdamentioning

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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“…iDNA6mA-Rice uses mononucleotide binary encoding and Random Forest to identify 6mA sites. Feature fusion makes use of diverse features to build prediction models and has been successfully and widely applied in bioinformatics (Zhang et al, 2018a;Zhang et al, 2018b;Zhou et al, 2019;Zhang et al, 2019a;Zhang et al, 2019b). In this paper, we propose a feature fusion-based method to identify 6mA sites in the rice genome, in which nucleotide chemical properties, binary encoding, KMER, and Markov features are used to formulate DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

et al. 2020

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Motivation: The biological function of N 6-methyladenine DNA (6mA) in plants is largely unknown. Rice is one of the most important crops worldwide and is a model species for molecular and genetic studies. There are few methods for 6mA site recognition in the rice genome, and an effective computational method is needed. Results: In this paper, we propose a new computational method called 6mA-Pred to identify 6mA sites in the rice genome. 6mA-Pred employs a feature fusion method to combine advantageous features from other methods and thus obtain a new feature to identify 6mA sites. This method achieved an accuracy of 87.27% in the identification of 6mA sites with 10-fold cross-validation and achieved an accuracy of 85.6% in independent test sets.

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The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions

Cited by 144 publications

References 33 publications

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

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

6mA-RicePred: A Method for Identifying DNA N6-Methyladenine Sites in the Rice Genome Based on Feature Fusion

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