Sequence-derived linear neighborhood propagation method for predicting lncRNA-miRNA interactions

Zhang, Wen; Tang, Guifeng; Wang, Siman; Chen, Yanlin; Zhou, Shuang; Li, Xiaohong

doi:10.1109/bibm.2018.8621184

Cited by 9 publications

(13 citation statements)

References 29 publications

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“…Previous studies [26, 29] and our experimental results demonstrate that interaction profiles are critical for predicting lncRNA-miRNA associations. However, interaction profiles of some lncRNAs (miRNAs) are unavailable.…”

Section: Resultssupporting

confidence: 57%

“…First, we calculate integrated lncRNA-lncRNA similarity and integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. As the extension of our previous work [29], we consider two integrated similarity calculation strategies, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are respectively implemented on two graphs to score lncRNA-miRNA pairs.…”

Section: Introductionmentioning

confidence: 99%

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LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination

et al. 2019

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BackgroundResearchers discover lncRNAs can act as decoys or sponges to regulate the behavior of miRNAs. Identification of lncRNA-miRNA interactions helps to understand the functions of lncRNAs, especially their roles in complicated diseases. Computational methods can save time and reduce cost in identifying lncRNA-miRNA interactions, but there have been only a few computational methods.ResultsIn this paper, we propose a sequence-derived linear neighborhood propagation method (SLNPM) to predict lncRNA-miRNA interactions. First, we calculate the integrated lncRNA-lncRNA similarity and the integrated miRNA-miRNA similarity by combining known lncRNA-miRNA interactions, lncRNA sequences and miRNA sequences. We consider two similarity calculation strategies respectively, namely similarity-based information combination (SC) and interaction profile-based information combination (PC). Second, the integrated lncRNA similarity-based graph and the integrated miRNA similarity-based graph are respectively constructed, and the label propagation processes are implemented on two graphs to score lncRNA-miRNA pairs. Finally, the weighted averages of their outputs are adopted as final predictions. Therefore, we construct two editions of SLNPM: sequence-derived linear neighborhood propagation method based on similarity information combination (SLNPM-SC) and sequence-derived linear neighborhood propagation method based on interaction profile information combination (SLNPM-PC). The experimental results show that SLNPM-SC and SLNPM-PC predict lncRNA-miRNA interactions with higher accuracy compared with other state-of-the-art methods. The case studies demonstrate that SLNPM-SC and SLNPM-PC help to find novel lncRNA-miRNA interactions for given lncRNAs or miRNAs.ConclusionThe study reveals that known interactions bring the most important information for lncRNA-miRNA interaction prediction, and sequences of lncRNAs (miRNAs) also provide useful information. In conclusion, SLNPM-SC and SLNPM-PC are promising for lncRNA-miRNA interaction prediction.

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Section: Resultssupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination

et al. 2019

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“…Generally, edges exist two similar sample nodes. The fast linear neighbor similarity approach (FLNSA) (Zhang et al, 2017(Zhang et al, , 2019) is a method to extract "sample-sample" similarity, which has been successfully applied to many bioinformatics classification tasks. In this study, FLNSA is utilized to calculate the similarity between samples.…”

Section: Graph Embeddings Network Constructionmentioning

confidence: 99%

m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

et al. 2021

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N6-methyladenosine (m6A) is one of the most prevalent RNA post-transcriptional modifications and is involved in various vital biological processes such as mRNA splicing, exporting, stability, and so on. Identifying m6A sites contributes to understanding the functional mechanism and biological significance of m6A. The existing biological experimental methods for identifying m6A sites are time-consuming and costly. Thus, developing a high confidence computational method is significant to explore m6A intrinsic characters. In this study, we propose a predictor called m6AGE which utilizes sequence-derived and graph embedding features. To the best of our knowledge, our predictor is the first to combine sequence-derived features and graph embeddings for m6A site prediction. Comparison results show that our proposed predictor achieved the best performance compared with other predictors on four public datasets across three species. On the A101 dataset, our predictor outperformed 1.34% (accuracy), 0.0227 (Matthew’s correlation coefficient), 5.63% (specificity), and 0.0081 (AUC) than comparing predictors, which indicates that m6AGE is a useful tool for m6A site prediction. The source code of m6AGE is available at https://github.com/bokunoBike/m6AGE.

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“…In this study, the Lagrange Multiplier method is introduced to obtain the minimum of Eq. (25). Let ψ = {ϕ ki } and = φ kj , the Lagrange multipliers ϕ ki and φ kj are used to constrain u ki ≥ 0 and v kj ≥ 0, respectively.…”

Section: ) Model Optimizationmentioning

confidence: 99%

“…To accelerate the process of identifying interactions between biomolecules, many computational methods have been proposed and effectively used for predicting relationships (e.g. miRNA-disease associations, protein-protein interactions and lncRNA-protein interactions), including manifold learning, manifold embedding and semisupervised learning, linear neighborhood propagation method, etc [21]- [25]. These computational methods for predicting miRNA-target interactions usually have the following common rules, including site accessibility, seed matching, free energy and protection [10].…”

Section: Introductionmentioning

confidence: 99%

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Wang

You

et al. 2020

IEEE Access

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Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in the regulation of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interaction computational prediction approach urgently need to be developed. In this work, we proposed a novel computational model, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately infer potential interactions between lncRNAs and miRNAs. To assess the performance of GNMFLMI, five-fold cross-validation experiments are carried out. The AUC values achieved by GNMFLMI on two datasets are 0.9769 and 0.8894, respectively, which outperform the compared methods. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

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Sequence-derived linear neighborhood propagation method for predicting lncRNA-miRNA interactions

Cited by 9 publications

References 29 publications

LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination

LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination

m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

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