MPGraph: multi‐view penalised graph clustering for predicting drug–target interactions

Liu, Limin

doi:10.1049/iet-syb.2013.0040

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…For each specific view, SVM with the corresponding Kronecker kernel is applied to solve drug–target prediction problem. For the multi‐view SVM method, we simply apply the SVM approach with multiple kernels from the two views. (b) BGL [2]: For either structural view or chemical view, BGL can be used to predict drug–target associations as a single‐view approach. (c) SPGraph and MPGraph [22]: SPGraph is a single‐view method to predict drug–target associations, and it can be used for either view. MPGraph is the extended multi‐view method, in which both two views can be integrated for drug–target prediction. (d) SLRE and MLRE [23]: SLRE is a low‐rank embedding based single‐view method, which can be used for either view.…”

Section: Experiments Resultsmentioning

confidence: 99%

Drug repositioning via matrix completion with multi‐view side information

2019

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In the process of drug discovery and disease treatment, drug repositioning is broadly studied to identify biological targets for existing drugs. Many methods have been proposed for drug–target interaction prediction by taking into account different kinds of data sources. However, most of the existing methods only use one side information for drugs or targets to predict new targets for drugs. Some recent works have improved the prediction accuracy by jointly considering multiple representations of drugs and targets. In this work, the authors propose a drug–target prediction approach by matrix completion with multi‐view side information (MCM) of drugs and proteins from both structural view and chemical view. Different from existing studies for drug–target prediction, they predict drug–target interaction by directly completing the interaction matrix between them. The experimental results show that the MCM method could obtain significantly higher accuracies than the comparison methods. They finally report new drug–target interactions for 26 FDA‐approved drugs, and biologically discuss these targets using existing references.

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Section: Experiments Resultsmentioning

confidence: 99%

Drug repositioning via matrix completion with multi‐view side information

2019

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“… 41 proposed multi-tensor decomposition (thus, by definition, an unsupervised method) and applied it to drug discovery; they could identify only compounds and could not identify any drug target genes because by means of their methodology, only features shared across multiple views (in their case compounds) can be screened. Although Li 42 proposed an integrated method that can identify drug–target protein interactions, the method is fully supervised because it cannot identify new drug–target protein interactions without any pre-knowledge.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets

Taguchi

2017

Sci Rep

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Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets proteins and not mRNAs, the mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I applied tensor-decomposition-based unsupervised feature extraction to the integrated analysis using a mathematical product of gene expression in various diseases and gene expression in the DrugMatrix dataset, where comprehensive data on gene expression during various drug treatments of rats are reported. I found that this strategy, in a fully unsupervised manner, enables researchers to identify a combined set of genes and compounds that significantly overlap with gene and drug interactions identified in the past. As an example illustrating the usefulness of this strategy in drug discovery experiments, I considered cirrhosis, for which no effective drugs have ever been proposed. The present strategy identified two promising therapeutic-target genes, CYPOR and HNFA4; for their protein products, bezafibrate was identified as a promising candidate drug, supported by in silico docking analysis.

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“…For example, in image/video processing, different visual descriptors such as Local Binary Patterns (LBP) (Ojala, Pietikainen, and Maenpaa 2002), Scale Invariant Feature Transform (SIFT) (Lowe and Lowe 2004) and Histogram of Oriented Gradient (HOG) (Dalal and Triggs 2005) are often used to describe each image/video frame from different views. In biomedical research, both the chemical structure and chemical response in different cells can be used to represent a certain drug, while the sequence and gene expression values can represent a certain protein in different aspects (Li 2014;Li and Cai 2017).…”

Section: Introductionmentioning

confidence: 99%

CGD: Multi-View Clustering via Cross-View Graph Diffusion

Tang

Liu

Zhu

et al. 2020

AAAI

118

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Graph based multi-view clustering has been paid great attention by exploring the neighborhood relationship among data points from multiple views. Though achieving great success in various applications, we observe that most of previous methods learn a consensus graph by building certain data representation models, which at least bears the following drawbacks. First, their clustering performance highly depends on the data representation capability of the model. Second, solving these resultant optimization models usually results in high computational complexity. Third, there are often some hyper-parameters in these models need to tune for obtaining the optimal results. In this work, we propose a general, effective and parameter-free method with convergence guarantee to learn a unified graph for multi-view data clustering via cross-view graph diffusion (CGD), which is the first attempt to employ diffusion process for multi-view clustering. The proposed CGD takes the traditional predefined graph matrices of different views as input, and learns an improved graph for each single view via an iterative cross diffusion process by 1) capturing the underlying manifold geometry structure of original data points, and 2) leveraging the complementary information among multiple graphs. The final unified graph used for clustering is obtained by averaging the improved view associated graphs. Extensive experiments on several benchmark datasets are conducted to demonstrate the effectiveness of the proposed method in terms of seven clustering evaluation metrics.

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MPGraph: multi‐view penalised graph clustering for predicting drug–target interactions

Cited by 16 publications

References 21 publications

Drug repositioning via matrix completion with multi‐view side information

Drug repositioning via matrix completion with multi‐view side information

Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets

CGD: Multi-View Clustering via Cross-View Graph Diffusion

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