GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network

Liu, Zhixian; Chen, Yi‐Ping Phoebe; Lan, Wei; Pan, Haiming; Hao, Xinkun; Pan, Shirui

doi:10.3389/fgene.2021.650821

Cited by 19 publications

(7 citation statements)

References 43 publications

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“…Therefore, the AUC and AUPR are usually adequate metrics for evaluating the performance of a model for DTI prediction [ 40 ]. Many similar studies have used these two metrics to evaluate the performance of methods for predicting DTIs [ 26 , 28 , 41 – 43 ]. As biologists often select drug-target pairs with high prediction scores for subsequent wet experiment validation, the recall rates of the top (5%, 10%, 15%, 20%, and 30%) proportion of candidate targets predicted by the model were selected.…”

Section: Resultsmentioning

confidence: 99%

“…To further evaluate the performance of SDGAE, we compared it with several other state-of-the-art methods, including GRMF [ 8 ], DTINet [ 9 ], GANDTI [ 28 ], NGDTP [ 7 ], MolTrans [ 19 ], and GADTI [ 26 ]. The hyperparameters of these methods were selected based on ranges recommended in the literature.…”

Section: Resultsmentioning

confidence: 99%

“…For example, Manoochehri et al [ 25 ] proposed an end-to-end model in which a heterogeneous network with seven types of edges, comprising drugs, proteins, and diseases, was constructed and graph convolution was performed for each edge type. Liu et al [ 26 ] also proposed a model, named GADTI, based on a graph convolutional autoencoder. The encoder in this model consists of the combination of a GCN and an RWR, which provides more information to the nodes, and DisMult [ 27 ] was used as the decoder.…”

Section: Introductionmentioning

confidence: 99%

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Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Chen

Zheng

2023

BMC Bioinformatics

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Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction methods. Results Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original space. Conclusions Overall, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Chen

Zheng

2023

BMC Bioinformatics

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“…In the past few decades, many multi-view biological data integration models based on graph learning, matrix decomposition, network fusion, deep learning, nuclear methods and other technologies have been designed and applied to a wide range of bioinformatics topics ( Li et al, 2016 ), such as prediction of drug–target interactions ( Liu et al, 2021 ), identification of cancer driver genes ( Bashashati et al, 2012 ) and genotype-phenotype interactions ( Qin et al, 2020 ). These studies provide meaningful insights into the cause and development of cancer.…”

Section: Discussionmentioning

confidence: 99%

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Liu

Cheng

et al. 2021

Front. Genet.

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It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data. In recent years, some multi-view clustering algorithms have been proposed and applied to the prediction of cancer subtypes. Among them, the multi-view clustering methods based on graph learning are widely concerned. These multi-view approaches usually have one or more of the following problems. Many multi-view algorithms use the original omics data matrix to construct the similarity matrix and ignore the learning of the similarity matrix. They separate the data clustering process from the graph learning process, resulting in a highly dependent clustering performance on the predefined graph. In the process of graph fusion, these methods simply take the average value of the affinity graph of multiple views to represent the result of the fusion graph, and the rich heterogeneous information is not fully utilized. To solve the above problems, in this paper, a Multi-view Spectral Clustering Based on Multi-smooth Representation Fusion (MRF-MSC) method was proposed. Firstly, MRF-MSC constructs a smooth representation for each data type, which can be viewed as a sample (patient) similarity matrix. The smooth representation can explicitly enhance the grouping effect. Secondly, MRF-MSC integrates the smooth representation of multiple omics data to form a similarity matrix containing all biological data information through graph fusion. In addition, MRF-MSC adaptively gives weight factors to the smooth regularization representation of each omics data by using the self-weighting method. Finally, MRF-MSC imposes constrained Laplacian rank on the fusion similarity matrix to get a better cluster structure. The above problems can be transformed into spectral clustering for solving, and the clustering results can be obtained. MRF-MSC unifies the above process of graph construction, graph fusion and spectral clustering under one framework, which can learn better data representation and high-quality graphs, so as to achieve better clustering effect. In the experiment, MRF-MSC obtained good experimental results on the TCGA cancer data sets.

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“…Based on L 2,1 norm graph regularization, Cui et al [22] used matrix factorization to predict DTIs. Liu et al [23] used a graph convolutional network (GCN) followed by a random walk with restart (RWR) to obtain features of the drugs and targets from the related heterozygous data, and a matrix factorization model (DistMult) to predict DTIs. Gao et al [24] proposed a collaborative matrix factorization method with soft regularization to predict DTIs.…”

Section: Introductionmentioning

confidence: 99%

NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

Zhang

Xie

2022

IEEE Access

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Accurate drug-target interactions (DTIs) prediction can significantly speed up the process of new drug design and development. Recently, many matrix factorization methods have been used to predict DTIs. However, most of them use heuristic and iterative strategies, and their convergence and performance can not be guaranteed. In order to accurately predict DTIs, we propose a new algorithm, NNDSVD-GRMF, our method is based on graph dual regularization non-negative matrix factorization (GDNMF) and nonnegative double singular value decomposition (NNDSVD), which considers both the initialization stage of the non-negative matrix factorization and the structural information of the data and features. At the same time, in order to improve the adaptability of the algorithm, the extension of the NNDSVD-GRMF (NNDSVD-WGRMF) is also proposed. Extensive experimental results show that our methods have better performance than other state-of-the-art methods. In the case studies, among the 10 highest-scoring drugs predicted to interact with androgen receptor, 9 drugs have been validated, and among the 10 highest-scoring target proteins predicted to be targeted by the drug nicotine bitartrate, 9 targets have been validated.

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GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network

Cited by 19 publications

References 43 publications

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

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