DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Thafar, Maha A.; Olayan, Rawan S.; Ashoor, Haitham; Albaradei, Somayah; Bajic, Vladimir B.; Gao, Xin; Gojobori, Takashi; Essack, Magbubah

doi:10.1186/s13321-020-00447-2

Cited by 95 publications

(85 citation statements)

References 91 publications

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“…The latter is related to finding nodes that share similar roles in different communities, achieved through Breadth-first search. Both DeepWalk and Node2vec embeddings are being used in the biomedical domain, such as for drug-target interaction prediction, and they were effective in producing the desired results (Zong et al, 2017;Thafar et al, 2020aThafar et al, , 2020b.…”

Section: Random Walk-based Embedding Methodsmentioning

confidence: 99%

Application and evaluation of knowledge graph embeddings in biomedical data

Alshahrani

Thafar

Essack

2021

PeerJ Computer Science

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Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search capabilities. However, linked data and bio-ontologies are more recently being used to represent information as multi-relational heterogeneous graphs, “knowledge graphs”. The reason being, entities and relations in the knowledge graph can be represented as embedding vectors in semantic space, and these embedding vectors have been used to predict relationships between entities. Such knowledge graph embedding methods provide a practical approach to data analytics and increase chances of building machine learning models with high prediction accuracy that can enhance decision support systems. Here, we present a comparative assessment and a standard benchmark for knowledge graph-based representation learning methods focused on the link prediction task for biological relations. We systematically investigated and compared state-of-the-art embedding methods based on the design settings used for training and evaluation. We further tested various strategies aimed at controlling the amount of information related to each relation in the knowledge graph and its effects on the final performance. We also assessed the quality of the knowledge graph features through clustering and visualization and employed several evaluation metrics to examine their uses and differences. Based on this systematic comparison and assessments, we identify and discuss the limitations of knowledge graph-based representation learning methods and suggest some guidelines for the development of more improved methods.

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Section: Random Walk-based Embedding Methodsmentioning

confidence: 99%

Application and evaluation of knowledge graph embeddings in biomedical data

Alshahrani

Thafar

Essack

2021

PeerJ Computer Science

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“…Thus, identifing novel drug-target interactions (DTIs) is an essential step in the drug discovery field, like drug repurposing [ 12 , 18 , 26 ]. However, transitional costly experiments limit the process to identify new DTIs [ 26 , 28 , 31 ]. Thus, the computational approach for DTI prediction is urgent [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Deep drug-target binding affinity prediction with multiple attention blocks

Zeng

Chen

Luo

et al. 2021

Briefings in Bioinformatics

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Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value $k \in \{3, 5\}$. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and $3137$ FDA-approved drugs.

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“…Thafar et al . [ 52 ] introduced a method DTiGEMS+ that predicts DTIs using graph mining graph embedding and similarity techniques. Their method combines feature-based and similarity-based approaches and models the prediction of potential DTIs as a link identification problem in a heterogeneous network.…”

Section: Introductionmentioning

confidence: 99%

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

Mahmud

Chen

Liu

et al. 2021

Briefings in Bioinformatics

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Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

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DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Cited by 95 publications

References 91 publications

Application and evaluation of knowledge graph embeddings in biomedical data

Application and evaluation of knowledge graph embeddings in biomedical data

Deep drug-target binding affinity prediction with multiple attention blocks

PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques

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