HeTDR: Drug repositioning based on heterogeneous networks and text mining

Jin, Shilei; Niu, Zhangming; Jiang, Changzhi; Huang, Wei; Xia, Feng; Jin, Xurui; Liu, Xiangrong; Zeng, Xiangxiang

doi:10.1016/j.patter.2021.100307

Cited by 15 publications

(13 citation statements)

References 62 publications

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“… Title Datasets used Repurposed Drugs Evaluation Criteria Tools used Ref. 1 Designing a Network Proximity-Based Drug Repurposing Strategy for COVID-19 BioGRID – Network proximity/ Network Diffusion Cytoscape, VarElect tool [169] 2 Network medicine framework for identifying drug-repurposing opportunities for COVID-19 13 Datasets, DrugBank, STRING 989 Drugs, 77 Validated in VeroE6 Cells, 76/77 validated in Human Cells Network proximity, network diffusion, Network AI Experimental, Ensembl algorithmic prediction [176] 3 Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis DGIDb, gene co-expression, DrugBank 5 Drugs Gene enrichment analysis for Genes and miRNA Node degree and centralities [195] 4 Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19 Uniprot, STRING, CMap, LINCS, GEO – Gene expression profiling CMap ranking [184] 5 Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2 STITCH, KEGG, BioGRID, PubChem, IID, – Enrichment analysis, Molecular docking DAVID, GOplot, AutoDock Vina, Cytoscape, Ligplot+ [196] 6 Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization with Kernel Diffusion – 4 Drugs 5-fold cross validations, AUC, AUPRs, recall, similarity diffusion Molecular docking [197] 7 HeTDR: Drug repositioning based on heterogeneous networks and text mining Mesh, DrugBank, PubMed 10 Drugs AUPR, AUROC, F1-measure – [198] 8 ...…”

Section: Network Based Studies For Covid-19mentioning

confidence: 99%

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed

Soomro

Salih

et al. 2022

Biomedicine & Pharmacotherapy

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Section: Network Based Studies For Covid-19mentioning

confidence: 99%

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed

Soomro

Salih

et al. 2022

Biomedicine & Pharmacotherapy

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“…In a recent work, Jin et al. [ 34 ] utilized similarity network fusion to merge multiple drug–drug networks constructed based on known drug–drug interactions, similarities based on its association with proteins and side effects, omics profiles and structural properties which were then used to retrieve drug features to be used in a drug-disease heterogeneous network for predicting drug-disease associations.…”

Section: Construction Of Biological Networkmentioning

confidence: 99%

“…Most existing methods utilize a few data sources to represent disease similarities. To include further disease-related features, HeTDR [ 34 ], a newly developed method, utilizes biomedical text mining [ 83 ] to elaborate an extensive set of disease features which improves the accuracy of drug-disease associations’ prediction. HeTDR also uses network affinity propagation in combination with spare autoencoders [ 84 ] to derive drug-related features.…”

Section: Ndm In Drug Discoverymentioning

confidence: 99%

Network approaches for modeling the effect of drugs and diseases

Rintala

Ghosh

Fortino

2022

Briefings in Bioinformatics

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The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

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“…Jin et al . [ 13 ] proposed an approach that integrates multiple heterogeneous networks to predict association scores of drugs and diseases. Their method combines drug and disease features retrieved from multiple drug networks and known drug-disease association networks, respectively.…”

Section: Introductionmentioning

confidence: 99%

NTD-DR: Nonnegative tensor decomposition for drug repositioning

et al. 2022

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Computational drug repositioning aims to identify potential applications of existing drugs for the treatment of diseases for which they were not designed. This approach can considerably accelerate the traditional drug discovery process by decreasing the required time and costs of drug development. Tensor decomposition enables us to integrate multiple drug- and disease-related data to boost the performance of prediction. In this study, a nonnegative tensor decomposition for drug repositioning, NTD-DR, is proposed. In order to capture the hidden information in drug-target, drug-disease, and target-disease networks, NTD-DR uses these pairwise associations to construct a three-dimensional tensor representing drug-target-disease triplet associations and integrates them with similarity information of drugs, targets, and disease to make a prediction. We compare NTD-DR with recent state-of-the-art methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR) and find that our method outperforms competing methods. Moreover, case studies with five diseases also confirm the reliability of predictions made by NTD-DR. Our proposed method identifies more known associations among the top 50 predictions than other methods. In addition, novel associations identified by NTD-DR are validated by literature analyses.

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HeTDR: Drug repositioning based on heterogeneous networks and text mining

Cited by 15 publications

References 62 publications

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Network approaches for modeling the effect of drugs and diseases

NTD-DR: Nonnegative tensor decomposition for drug repositioning

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