A network-based model for drug repurposing in Rheumatoid Arthritis

Kim, Ki-Jo; Rai, Navneet; Kim, Minseung; Tagkopoulos, Ilias

doi:10.1101/335679

Cited by 2 publications

(2 citation statements)

References 72 publications

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“…For the four benchmark diseases considered in our study, studies on repurposed drugs for tumors are reported in [Nowak-Sliwinska et al, 2019], [Zhang et al, 2020b], [Turanli et al, 2018], for asthma in [Kruse and Vanijcharoenkarn, 2018], and for rheumatoid arthritis in [Hu et al, 2019], and [Kim et al, 2018].…”

Section: Related Workmentioning

confidence: 99%

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Lucchetta

Pellegrini

2021

Preprint

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Computational Drug Repositioning aims at ranking and selecting existing drugs for use in novel diseases or existing diseases for which these drugs were not originally designed. Using vast amounts of available omic data in digital form within an in silico screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of Disease Active Subnetwork construction algorithms. DrugMerge uses differential transcriptomic data from cell lines/tissues of patients affected by the disease and differential transcriptomic data from drug perturbation assays, in the context of a large gene co-expression network. Experiments with four benchmark diseases (Asthma, Rheumatoid Arthritis, Prostate Cancer, and Colorectal Cancer) demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Our method is competitive with the state-of-the-art tools such as CMAP (Connectivity Map). Application of DrugMerge to COVID-19 data found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge is able to mimic human expert judgment

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Section: Related Workmentioning

confidence: 99%

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Lucchetta

Pellegrini

2021

Preprint

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“…And genes associated with the same disorder often share cellular localization, functions [214] and are highly co-expressed [218]. Therefore, a subnetwork-called disease module-that can define a disease or a phenotype can be uncovered [214,215,217,[219][220][221][222].…”

Section: Network Medicinementioning

confidence: 99%

Construction, comparison and evolution of networks in life sciences and other disciplines

Gysi

Nowick

2020

J. R. Soc. Interface.

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Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

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A network-based model for drug repurposing in Rheumatoid Arthritis

Abstract: Background

Cited by 2 publications

References 72 publications

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Drug Repositioning by Merging Active Subnetworks Validated in Cancer and COVID-19

Construction, comparison and evolution of networks in life sciences and other disciplines

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