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

Wang, Mei-Neng; You, Zhu‐Hong; Li, Liping; Wong, Leon; Chen, Zhan-Heng; Cheng-zhi, Gan

doi:10.1109/access.2020.2974349

Cited by 27 publications

(13 citation statements)

References 60 publications

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“…To explore their core functionality and key roles in diverse biological and pathological processes, determining the interaction between them is indispensable. In the race to develop more robust and generalized lncRNA-miRNA interaction predictors, predominant computational approaches [6,14,19,20,43,43,74,78,90,91,93,96,97,[100][101][102][103] rely on some kind of known intrinsic information (eg expression profile similarity network, functional similarity) to determine the interaction between lncRNAs and miRNAs. The more comprehensive the information, the better the model identifies potential lncRNA-miRNA interactions.…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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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Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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“…Since the original data matrix in the real world usually contains very complex information, the NMF method based on a one-layer structure is difficult to mine the high-level features of the data [29]. Inspired by recent advances in deep learning, Trigeorgis et al [22] proposed a Deep semi-Nonnegative Matrix Factorization (Deep semi-NMF) algorithm, which can construct a deep network by factorizing the data many times through the semi-NMF method [31], so that the relationships between the different layers can be exploited to reveal the intrinsic high-level features of the original data.…”

Section: Deep Semi-nonnegative Matrix Factorizationmentioning

confidence: 99%

Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering

Guo

2021

IEEE Access

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Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention. This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the complex data. However, two limitations may still deteriorate their performances: (1) the local invariance of the input data is insufficiently explored, that is, the intrinsic geometrical structures of the original data in the data and feature spaces are not considered simultaneously; (2) the sparseness that can greatly improve the ability of learning parts is also ignored. In this paper, we propose a novel approach to address the above two problems, referred to as Sparse Dual Graph-regularized Deep Nonnegative Matrix Factorization (SDG Deep NMF), which can learn sparse and informative deep features while sufficiently exploring the local invariance of the data to discover valuable information underlying the input data. Specifically, SDG Deep NMF learns the informative deep features by performing the dual graph regularization in the deep NMF framework, which can respect the intrinsic geometrical structures of the input data in the data and feature spaces while mining the data information in hidden layers. Meanwhile, SDG Deep NMF also imposes sparse constraints on the basis matrix during the feature learning to improve the part-based learning capabilities. Moreover, we construct the objective function of SDG Deep NMF in the form of the Euclidean distance for convenience, the iterative updating scheme is chosen to optimize it. Comprehensive experiments on four benchmark datasets can demonstrate the effectiveness of the proposed approach in image clustering. INDEX TERMSDeep nonnegative matrix factorization, dual graph regularization, sparse constraints, image clustering.

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

Cited by 27 publications

References 60 publications

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

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

Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering

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

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