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

Zhang, Wen; Tang, Guifeng; Zhou, Shuang; Niu, Yanqing

doi:10.1186/s12864-019-6284-y

Cited by 38 publications

(31 citation statements)

References 56 publications

(48 reference statements)

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“…The nearest neighborhood information of biological entities in the association network can improve the prediction performance (Zhang et al, 2019a,b,c). For example, Zhang et al (2019a), Zhang et al (2019b), and Zhang et al (2019c) used neighborhood information and effectively found microRNA-disease associations, drug-drug interactions and long non-coding RNA-miRNA interactions. Therefore, we integrated neighborhood information to the above optimization model and built the final LMFNR model by Equation 14:…”

Section: Mda Prediction Based On Lmfnrmentioning

confidence: 99%

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

et al. 2020

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Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.

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Section: Mda Prediction Based On Lmfnrmentioning

confidence: 99%

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

et al. 2020

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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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“…There are complex interactions between lncRNAs and miRNAs, such as adsorption, inhibition, competition, etc., [19]. Recently, more and more lncRNA-miRNA interactions have been disclosed by many research efforts [20], [21]. Therefore, in cancer classification, the pure study of the independent regulation of one or more NCGs on PCGs, without considering the interaction between different types of NCGs and the joint regulation of the NCGs' interaction on PCGs, will lead to the loss of key association information for classification and the incapacity of accurately reappearing the complex mechanism of cancer development.…”

Section: A Dataset Constructionmentioning

confidence: 99%

A Cancer Diagnosis Method Combining miRNA-lncRNA Interaction Pairs and Class Weight Competition

et al. 2020

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From the perspective of data science, we propose a cancer diagnosis method combining miRNA-lncRNA interaction pairs and class weight competition. First, miRNA-lncRNA interaction data is introduced into joint expression profiles, and the complex mechanism of cancer development is demonstrated in depth through the reappearance of key association information. This is an information ensemble of three carcinogenic mechanisms at dataset construction level: classical genetics, epigenetics, and the complex interaction effect between miRNAs and lncRNAs. Then, we put forward a hybrid feature selection algorithm. By preserving the interaction relationship between miRNAs and lncRNAs, it quickly and steadily removes irrelevant and redundant features and solves the high-dimensional disaster problem of cancer expression profiles. This is an information ensemble of multiple feature selection algorithms and the significant association relationship found between multi-dimensional features at feature selection level. A diversity sampling and multi-algorithm learners are used to construct a multiple heterogeneous classification models, which overcomes the small size of normal samples and the local optimum of single algorithm and single mode. This is an information ensemble of multiple classification model structures and multiple classification model state parameters at classification modeling level. At decision level, the proposed class weight which does not depend on the sample size is constructed to address the issue of unbalanced sample class of cancers. The ensemble of multi-category multi-state information at four levels (dataset construction, feature selection, classification modeling, and decision) constitutes the framework of the proposed method. We classify BRCA, LUAD and LUSC in TCGA. Compared with the state-of-the-art classification methods, the proposed method has improved classification accuracy by 9.25%∼21.25%, sensitivity by 6.45%∼66.45%, and specificity by 10.11%. In addition, we find that lincRNA instead of miRNA always appears in each group of feature genes, which provides a new clue for the locus target selection in cancer treatment. INDEX TERMS Cancer diagnosis, joint expression profiles, miRNA-lncRNA, feature selection embedded interaction pairs, class weight competition, locus target discovery.

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

Cited by 38 publications

References 56 publications

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

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

A Cancer Diagnosis Method Combining miRNA-lncRNA Interaction Pairs and Class Weight Competition

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