Heterogeneous graph inference based on similarity network fusion for predicting lncRNA–miRNA interaction

Fan, Yongxian; Cui, Juan; Zhu, Qingqi

doi:10.1039/c9ra11043g

Cited by 12 publications

(11 citation statements)

References 62 publications

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“…3B ). For the SNFHGILMI dataset provided by Fan, Cui & Zhu (2020) , the average AUC values were 0.8653 ± 0.0021, 0.8570 ± 0.0012, and 0.9426 ± 0.0035, respectively ( Fig. 3C ).…”

Section: Resultsmentioning

confidence: 97%

“…We calculated the structural consistency of three datasets (i.e., the EPLMI dataset provided by Huang, Chan & You (2018) , SNFHGILMI dataset provided by Fan, Cui & Zhu (2020) , and SPMLMI dataset obtained in this study). Each group of datasets was composed of an miRNA similarity network ( MSnet ), an lncRNA similarity network ( LSnet ), and a bilayer network ( Bilayer − net ).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, we compared SPMLMI with the previously established EPLMI ( Huang, Chan & You, 2018 ) based on the two-way diffusion algorithm and SNFHGILMI ( Fan, Cui & Zhu, 2020 ) based on the heterogeneous graph inference method. Initially, we evaluated the same dataset (i.e., the SPMLMI dataset obtained in this study, see ‘Materials & Methods’ for details) by these three methods.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, we applied SPMLMI to datasets analyzed by two additional methods, EPLMI ( Huang, Chan & You, 2018 ) and SNFHGILMI ( Fan, Cui & Zhu, 2020 ), for an objective comparison of performance.…”

Section: Methodsmentioning

confidence: 99%

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SPMLMI: predicting lncRNA–miRNA interactions in humans using a structural perturbation method

Chen

et al. 2021

PeerJ

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Long non-coding RNA (lncRNA)–microRNA (miRNA) interactions are quickly emerging as important mechanisms underlying the functions of non-coding RNAs. Accordingly, predicting lncRNA–miRNA interactions provides an important basis for understanding the mechanisms of action of ncRNAs. However, the accuracy of the established prediction methods is still limited. In this study, we used structural consistency to measure the predictability of interactive links based on a bilayer network by integrating information for known lncRNA–miRNA interactions, an lncRNA similarity network, and an miRNA similarity network. In particular, by using the structural perturbation method, we proposed a framework called SPMLMI to predict potential lncRNA–miRNA interactions based on the bilayer network. We found that the structural consistency of the bilayer network was higher than that of any single network, supporting the utility of bilayer network construction for the prediction of lncRNA–miRNA interactions. Applying SPMLMI to three real datasets, we obtained areas under the curves of 0.9512 ± 0.0034, 0.8767 ± 0.0033, and 0.8653 ± 0.0021 based on 5-fold cross-validation, suggesting good model performance. In addition, the generalizability of SPMLMI was better than that of the previously established methods. Case studies of two lncRNAs (i.e., SNHG14 and MALAT1) further demonstrated the feasibility and effectiveness of the method. Therefore, SPMLMI is a feasible approach to identify novel lncRNA–miRNA interactions underlying complex biological processes.

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“…3B ). For the SNFHGILMI dataset provided by Fan, Cui & Zhu (2020) , the average AUC values were 0.8653 ± 0.0021, 0.8570 ± 0.0012, and 0.9426 ± 0.0035, respectively ( Fig. 3C ).…”

Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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SPMLMI: predicting lncRNA–miRNA interactions in humans using a structural perturbation method

Chen

et al. 2021

PeerJ

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“…Finally, a random forest predictor was used to infer potential interactions among miRNA and lncRNA sequences. Similarly, there exist several other lncRNA-miRNA interaction prediction approaches that leverage known intrinsic information of lncRNA and miRNA sequences to determine lncRNA-miRNA interaction in various species [6,14,19,20,43,43,74,78,90,91,93,96,97,[100][101][102][103].…”

Section: Introductionmentioning

confidence: 99%

BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction

Asim

Ibrahim

Zehe

et al. 2022

Interdiscip Sci Comput Life Sci

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Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences. Method The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction. Results BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%. Conclusion In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process. Availability: BoT-Net web server can be accessed at https://sds_genetic_analysis.opendfki.de/lncmiRNA/. Graphic Abstract

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Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA–lncRNA Interaction Prediction

Kang

Meng

Shi

et al. 2021

Interdiscip Sci Comput Life Sci

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Heterogeneous graph inference based on similarity network fusion for predicting lncRNA–miRNA interaction

Abstract: LncRNA and miRNA are two non-coding RNA types that are popular in current research.

Cited by 12 publications

References 62 publications

SPMLMI: predicting lncRNA–miRNA interactions in humans using a structural perturbation method

SPMLMI: predicting lncRNA–miRNA interactions in humans using a structural perturbation method

BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction

Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA–lncRNA Interaction Prediction

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