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GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions
[article]
2019
bioRxiv
pre-print
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In this work, we proposed a novel computational method, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. ...
Finally, a graph regularized nonnegative matrix factorization model is developed to accurately identify potential interactions between lncRNAs and miRNAs. ...
Graph regularized nonnegative matrix factorization for predicting lncRNA-miRNA interactions (GNMFLMI)
methods overview In this study，we propose a new calculation model, GNMFLMI, to predict lncRNA-miRNA ...
doi:10.1101/835934
fatcat:5j3pwcosbzfu7p4f36pobawdhy
Editorial: Machine Learning Techniques on Gene Function Prediction
2019
Frontiers in Genetics
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Zhao et al. employed random walk and neighborhood regularized logistic matrix factorization approach. Dai et al. paid attention to complex features for ncRNA-protein interaction prediction. ...
He et al. proposed an NRLMFMDA (neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction) by integrating miRNA functional similarity, disease semantic similarity ...
No use, distribution or reproduction is permitted which does not comply with these terms. ...
doi:10.3389/fgene.2019.00938
pmid:31636657
pmcid:PMC6788354
fatcat:lamt7sn6dnguvn5usuyo6ls7rm
Prediction of circRNA-miRNA Associations Based on Network Embedding
2021
Complexity
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Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. ...
In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. ...
Finally, the weighted neighborhood regularized logistic matrix factorization and the inner product are utilized to reconstruct the circRNA-miRNA association matrix based on the circRNA feature vector and ...
doi:10.1155/2021/6659695
fatcat:6r4wcnn7pbbxpachee3tjm67k4
Probing lncRNA–Protein Interactions: Data Repositories, Models, and Algorithms
2020
Frontiers in Genetics
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Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. ...
We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. ...
FIGURE 7 | 7 Flowchart of LPI prediction model based on neighborhood regularized logistic matrix factorization. ...
doi:10.3389/fgene.2019.01346
pmid:32082358
pmcid:PMC7005249
fatcat:nwcefem27jan3m5payw5odunna
Computational methods and applications for identifying disease-associated lncRNAs as potential biomarkers and therapeutic targets
2020
Molecular Therapy: Nucleic Acids
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In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. ...
Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. ...
, which is anchored in the neighborhood-regularized logistic matrix factorization and optimizes the above parameters to predict interaction probabilities. 73 NNLDA, determined by Hu et al., 74 solved ...
doi:10.1016/j.omtn.2020.05.018
pmid:32585624
pmcid:PMC7321789
fatcat:qihynfkmgvhddjndjyc2svuscm
DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization
2019
Genes
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The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been achieved. ...
We firstly applied DNILMF to lncRNA–disease association prediction (DNILMF-LDA). ...
Acknowledgments: The authors thank Xiaofang Xiao for assistance with the experiments.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/genes10080608
pmid:31409034
pmcid:PMC6722840
fatcat:qk65tioihzh7pnan64tkhczlgq
GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network
2020
Frontiers in Genetics
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learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. ...
Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. ...
KBMF-MDI predicts the association between miRNAs and diseases based on their similarities to diseases , and this is a method that is based on the dynamic neighborhood regularized logical matrix factorization ...
doi:10.3389/fgene.2020.00272
pmid:32351537
pmcid:PMC7174746
fatcat:t3sfguybbjgatcr4yklvmgpyqa
Predicting Pseudogene–miRNA Associations Based on Feature Fusion and Graph Auto-Encoder
2021
Frontiers in Genetics
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Here, we propose a prediction model PMGAE (Pseudogene–MiRNA association prediction based on the Graph Auto-Encoder), which incorporates feature fusion, graph auto-encoder (GAE), and eXtreme Gradient Boosting ...
Numerous studies have shown that pseudogenes and miRNAs have interactions and form a ceRNA network with mRNA to regulate biological processes and involve diseases. ...
respectively introduced methods based on collaborative matrix factorization and neighborhood-regularized logistic matrix factorization to predict drug-target interactions (Zheng et al., 2013; Liu Y. et ...
doi:10.3389/fgene.2021.781277
pmid:34966413
pmcid:PMC8710693
fatcat:hsn2cpmsd5czrjt6o27jpdfd6u
Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma
2021
PLoS ONE
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Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). ...
The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset. ...
With the development of bioinformatics, some new prediction methods, such as the lncRNA-miRNA interactions prediction by logistic matrix factorization with neighborhood regularized (LMFNRLMI), enable us ...
doi:10.1371/journal.pone.0260876
pmid:34879089
pmcid:PMC8654225
fatcat:i4faxq6wtjfcbf7pqpfsquncle
Construct a molecular associations network to systematically understand intermolecular associations in Human cells
[article]
2019
bioRxiv
pre-print
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To further evaluate the performance of our method, a case study for predicting lncRNA-protein interactions was executed. ...
Our method achieves a remarkable performance on entire molecular associations network with an AUC of 0.9552 and an AUPR of 0.9338. ...
SDNE to predict lncRNA-protein interactions was carried out. ...
doi:10.1101/693051
fatcat:66ent6m7frel5njek2bizrc6eq
Prediction of miRNA-Disease Association Using Deep Collaborative Filtering
2021
BioMed Research International
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The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. ...
To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. ...
[50] integrated neighborhood constraint with matrix completion and proposed a computational model based neighborhood constraint matrix completion for miRNA-disease association (NCMCMDA) prediction. ...
doi:10.1155/2021/6652948
pmid:33681362
pmcid:PMC7929672
fatcat:xquft2vuhbdslbckepumxmo3ua
A novel lncRNA-miRNA-mRNA triple network identifies lncRNA TWF1 as an important regulator of miRNA and gene expression in coronary artery disease
2019
Nutrition & Metabolism
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This investigation on the regulatory networks of lncRNA-miRNA-mRNA in CAD suggests that a novel lncRNA, lncRNA TWF1 is a risk factor for CAD, and expands our understanding into the mechanisms involved ...
A CAD lncRNA-miRNA-mRNA network was constructed which included 15 mRNAs, 3 miRNAs, 19 edges and one lncRNA. Nomogram showed that four ncRNAs were the risk of CAD. ...
The nomogram was constructed based on logistic regression model for outcome of definite CAD. ...
doi:10.1186/s12986-019-0366-3
pmid:31182968
pmcid:PMC6555741
fatcat:237aez4kbbesvkrql5bke3oxz4
LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification
2021
BMC Bioinformatics
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First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). ...
LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively ...
[24] identified new LPIs combing neighborhood regularized logistic matrix factorization. Zhao et al. ...
doi:10.1186/s12859-021-04399-8
pmid:34607567
fatcat:tu4jp6fevrhsrkkeyro3qgoeri
ncRNA-disease association prediction based on sequence information and tripartite network
2018
BMC Systems Biology
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Our proposed algorithm was evaluated based on a 5-fold-cross-validation with optimal kernel parameter tuning. ...
based upon sequence expressions with weights obtained from a multi-layer resource allocation technique. ...
[14] developed hypergeometric distribution for lncRNA-disease association inference (HGLDA) to predict lncRNA-disease associations by integrating miRNA-disease interactions and lncRNA-miRNA associations ...
doi:10.1186/s12918-018-0527-4
pmid:29671405
pmcid:PMC5907179
fatcat:qmtrexyelrckbldlfull4bgj4m
ILPMDA: Predicting miRNA–Disease Association Based on Improved Label Propagation
2021
Frontiers in Genetics
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To evaluate the prediction performance of ILPMDA, two types of cross-validation methods and case studies on three significant human diseases were implemented to determine the accuracy and effectiveness ...
In this study, we presented an improved label propagation for miRNA–disease association prediction (ILPMDA) method to observe disease-related miRNAs. ...
HLPMDA applied the heterogeneous label propagation algorithm on a multi-network of miRNAs, lncRNAs, and diseases to predict unobserved miRNA-disease interactions. ...
doi:10.3389/fgene.2021.743665
pmid:34659364
pmcid:PMC8514753
fatcat:2nwq3xnembfqtes36gfffjpi3m
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