DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

Su, Yinchun; Wu, Jiashuo; Li, Xiangmei; Li, Ji; Xin, Zhao; Pan, Bingyue; Huang, Junling; Kong, Qingfei; Han, Junwei

doi:10.1016/j.compbiomed.2023.106969

Cited by 9 publications

(10 citation statements)

References 64 publications

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“…The simplest method involves application of neighbourhood models from collaborative filtering to this class of problems [69]. A slightly more sophisticated approach is to use network diffusion based approaches [70, 71]. Such methods are unfortunately not as accurate as matrix completion based techniques; furthermore current network based techniques are formulated as matrix completion and not network diffusion for the same reason.…”

Section: Methodsmentioning

confidence: 99%

WITHDRAWN: Antibiotic Bacteria Interaction: Dataset and Benchmarking

Chatterjee,

Majumdar,

Chouzenoux

2024

Preprint

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This study introduces a dataset for drug-bacteria associations (DBA) that affects humans. Our contribution extends beyond merely curating the association matrix; we also conduct genomic similarity computations for all known bacteria impacting humans and assess structural similarities for all antibiotic drugs. This is a first of a kind dataset serving the fundamental goal of streamlining antibiotic repurposing for bacterial infections. We conducted thorough benchmarking on this dataset using state-of-the-art drug repurposing models. Furthermore, we applied this dataset to three case studies involving drug-resistant bacterial infections. Our results indicate that the dataset coupled with the algorithms are indeed effective for repurposing drugs with the potential for treating drug resistant bacterial infections.

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Section: Methodsmentioning

confidence: 99%

WITHDRAWN: Antibiotic Bacteria Interaction: Dataset and Benchmarking

Chatterjee,

Majumdar,

Chouzenoux

2024

Preprint

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“…We used over-representation analysis (Boyle et al, 2004) to identify biological processes from GO and Disease enriched terms from DisGeNET (Piñero et al, 2019) associated with these nodes. Next, for both networks we performed a Drug Target Set Enrichment Analysis (DTSEA), using the respective R package (Su et al, 2023), to identify the available drugs and diseases according to each network. We performed semantic similarity analysis among the nodes of each network and compared their distribution by Wilcoxon test.…”

Section: Enrichment Analysismentioning

confidence: 99%

Integrated systems biology approach identifies gene targets for endothelial dysfunction

Pinheiro‐de‐Sousa,

Fonseca‐Alaniz,

Giudice

et al. 2023

Molecular Systems Biology

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Endothelial dysfunction (ED) is critical in the development and progression of cardiovascular (CV) disorders, yet effective therapeutic targets for ED remain elusive due to limited understanding of its underlying molecular mechanisms. To address this gap, we employed a systems biology approach to identify potential targets for ED. Our study combined multi omics data integration, with siRNA screening, high content imaging and network analysis to prioritise key ED genes and identify a pro‐ and anti‐ED network. We found 26 genes that, upon silencing, exacerbated the ED phenotypes tested, and network propagation identified a pro‐ED network enriched in functions associated with inflammatory responses. Conversely, 31 genes ameliorated ED phenotypes, pointing to potential ED targets, and the respective anti‐ED network was enriched in hypoxia, angiogenesis and cancer‐related processes. An independent screen with 17 drugs found general agreement with the trends from our siRNA screen and further highlighted DUSP1, IL6 and CCL2 as potential candidates for targeting ED. Overall, our results demonstrate the potential of integrated system biology approaches in discovering disease‐specific candidate drug targets for endothelial dysfunction.

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“…Three of them, Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways, suggesting their potentials as promising inhibitors for COVID-19. 107 Moreover, several network-based methods, such as NBI and Path-based methods, were also applied in the drug repositioning for various diseases, including cancer, 108 COVID-19, 109 type 2 diabetes, 110 and Alzheimer's disease. 111 In 2022, we proposed a new method, 109 named NEDNBI (newly emerged disease via network-based inference), and applied it in the gene−disease−drug heterogeneous network.…”

Section: Prediction and Elucidation Of Mechanisms Of Therapeutic Effe...mentioning

confidence: 99%

“…As a common strategy, drug repositioning has been widely used to explore new therapeutic effects of old drugs . In this field, RWR methods were widely applied in the heterogeneous network for drug repositioning. − In 2023, Su et al proposed a new method, named DTSEA (drug target set enrichment analysis), which employs the RWR algorithm in the gene functional interaction network and performs enrichment analysis based on the disease-gene association and DTI for drug repositioning. DTSEA predicted 50 potential drug candidates for COVID-19, and 18 of them were evaluated in clinical trials.…”

Section: Applications In Drug Discoverymentioning

confidence: 99%

“…DTSEA predicted 50 potential drug candidates for COVID-19, and 18 of them were evaluated in clinical trials. Three of them, Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways, suggesting their potentials as promising inhibitors for COVID-19 . Moreover, several network-based methods, such as NBI and Path-based methods, were also applied in the drug repositioning for various diseases, including cancer, COVID-19, type 2 diabetes, and Alzheimer’s disease .…”

Section: Applications In Drug Discoverymentioning

confidence: 99%

See 1 more Smart Citation

Network-Based Methods and Their Applications in Drug Discovery

Yu,

Wu,

Wang

et al. 2023

J. Chem. Inf. Model.

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Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of networkbased methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

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DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

Cited by 9 publications

References 64 publications

WITHDRAWN: Antibiotic Bacteria Interaction: Dataset and Benchmarking

WITHDRAWN: Antibiotic Bacteria Interaction: Dataset and Benchmarking

Integrated systems biology approach identifies gene targets for endothelial dysfunction

Network-Based Methods and Their Applications in Drug Discovery

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