Kernel Soft-neighborhood Network Fusion for MiRNA-Disease Interaction Prediction

Ma, Yingiun; Ge, Leixin; Ma, Yuanyuan; Jiang, Xingpeng; He, Tingting; Hu, Xiaohua

doi:10.1109/bibm.2018.8621122

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…It should be noted that for the interaction prediction problem, all unknown interactions are to be detected [19–21,26]. Therefore, regarding CV a , in order to make test set for each cross‐validation contain all metabolite–disease pairs to be tested, we set the test set to 1/5 of known interactions and all unknown interactions.…”

Section: Resultsmentioning

confidence: 99%

“…Specifically, firstly, we use weighted K nearest neighbor profiles (WKNNP) [18] to preliminarily complete the interaction matrix A to obtain

\bar{A}

. Secondly, based on the complementary interaction network, we calculate the kernel neighborhood similarity [19–21] of metabolites and diseases, respectively. Finally, by using clusDCA [22,23], we fused semantic similarity and kernel neighborhood similarity of diseases to get the fusion disease similarity network SD, and fused functional similarity and kernel neighborhood similarity of metabolites to get the fusion similarity network SM of metabolites.…”

Section: Methodsmentioning

confidence: 99%

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Multi‐network logistic matrix factorization for metabolite–disease interaction prediction

Jiang

2020

FEBS Letters

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Edited by Qinghua CuiIdentifying disease-related metabolites is of great significance for the diagnosis, prevention, and treatment of disease. In this study, we propose a novel computational model of multiple-network logistic matrix factorization (MN-LMF) for predicting metabolite-disease interactions, which is especially relevant for new diseases and new metabolites. First, MN-LMF builds disease (or metabolite) similarity network by integrating heterogeneous omics data. Second, it combines these similarities with known metabolite-disease interaction networks, using modified logistic matrix factorization to predict potential metabolite-disease interactions. Experimental results show that MN-LMF accurately predicts metabolite-disease interactions, and outperforms other state-of-the-art methods. Moreover, case studies also demonstrated the effectiveness of the model to infer unknown metabolite-disease interactions for novel diseases without any known associations.

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Section: Resultsmentioning

confidence: 99%

“…Specifically, firstly, we use weighted K nearest neighbor profiles (WKNNP) [18] to preliminarily complete the interaction matrix A to obtain

\bar{A}

Section: Methodsmentioning

confidence: 99%

Multi‐network logistic matrix factorization for metabolite–disease interaction prediction

Jiang

2020

FEBS Letters

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“…Therefore, drawing on the method of Xiao et al 23 , based on S h seq and S v seq , we utilize weighted k nearest neighbor profiles (WKNNP) to initially complete the trained Y to obtain Y . In previous studies, we proposed a network construction method based on kernel neighborhood similarity (KSNS) 24,25 , which can hierarchically integrate neighborhood and non-neighborhood information and mine nonlinear relationships of samples, and has been well applied in some biological relationship prediction problems 20,21,26,27 . KSNS calculates the similarity as follows:…”

Section: Methods Reviewmentioning

confidence: 99%

Kernel Bayesian nonlinear matrix factorization based on variational inference for human–virus protein–protein interaction prediction

Ma,

Zhao,

2024

Sci Rep

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Identification of potential human–virus protein–protein interactions (PPIs) contributes to the understanding of the mechanisms of viral infection and to the development of antiviral drugs. Existing computational models often have more hyperparameters that need to be adjusted manually, which limits their computational efficiency and generalization ability. Based on this, this study proposes a kernel Bayesian logistic matrix decomposition model with automatic rank determination, VKBNMF, for the prediction of human–virus PPIs. VKBNMF introduces auxiliary information into the logistic matrix decomposition and sets the prior probabilities of the latent variables to build a Bayesian framework for automatic parameter search. In addition, we construct the variational inference framework of VKBNMF to ensure the solution efficiency. The experimental results show that for the scenarios of paired PPIs, VKBNMF achieves an average AUPR of 0.9101, 0.9316, 0.8727, and 0.9517 on the four benchmark datasets, respectively, and for the scenarios of new human (viral) proteins, VKBNMF still achieves a higher hit rate. The case study also further demonstrated that VKBNMF can be used as an effective tool for the prediction of human–virus PPIs.

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“…Based on these feature vectors, there are many methods for calculating similarities, such as Gaussian, linear neighborhood similarity (Zhang et al, 2018a) (LNS), and so on. Here, we adopt kernel neighborhood similarity (KSNS) (Ma et al, 2018a; Ma et al, 2018b), which not only considers the neighbor and non-neighbor similarity of samples hierarchically, but also explores nonlinear relations, which was well applied to a variety of biological problems. It should be noted that the currently known lncRNA-protein interaction matrix is incomplete.…”

Section: Methodsmentioning

confidence: 99%

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction

Jiang

2019

Front. Genet.

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Many long ncRNAs (lncRNA) make their effort by interacting with the corresponding RNA-binding proteins, and identifying the interactions between lncRNAs and proteins is important to understand the functions of lncRNA. Compared with the time-consuming and laborious experimental methods, more and more computational models are proposed to predict lncRNA-protein interactions. However, few models can effectively utilize the biological network topology of lncRNA (protein) and combine its sequence structure features, and most models cannot effectively predict new proteins (lncRNA) that do not interact with any lncRNA (proteins). In this study, we proposed a projection-based neighborhood non-negative matrix decomposition model (PMKDN) to predict potential lncRNA-protein interactions by integrating multiple biological features of lncRNAs (proteins). First, according to lncRNA (protein) sequences and lncRNA expression profile data, we extracted multiple features of lncRNA (protein). Second, based on protein GO ontology annotation, lncRNA sequences, lncRNA(protein) feature information, and modified lncRNA-protein interaction network, we calculated multiple similarities of lncRNA (protein), and fused them to obtain a more accurate lncRNA(protein) similarity network. Finally, combining the similarity and various feature information of lncRNA (protein), as well as the modified interaction network, we proposed a projection-based neighborhood non-negative matrix decomposition algorithm to predict the potential lncRNA-protein interactions. On two benchmark datasets, PMKDN showed better performance than other state-of-the-art methods for the prediction of new lncRNA-protein interactions, new lncRNAs, and new proteins. Case study further indicates that PMKDN can be used as an effective tool for lncRNA-protein interaction prediction.

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Kernel Soft-neighborhood Network Fusion for MiRNA-Disease Interaction Prediction

Cited by 8 publications

References 14 publications

Multi‐network logistic matrix factorization for metabolite–disease interaction prediction

Multi‐network logistic matrix factorization for metabolite–disease interaction prediction

Kernel Bayesian nonlinear matrix factorization based on variational inference for human–virus protein–protein interaction prediction

Projection-Based Neighborhood Non-Negative Matrix Factorization for lncRNA-Protein Interaction Prediction

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