DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

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Zheng , S , Aldahdooh , J , Shadbahr , T , Wang , Y , Aldahdooh , D , Bao , J , Wang , W & Tang , J 2021 , ' DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal ' , Nucleic Acids Research , vol. 49 , no. W1 , pp. W174-W184 . https://doi.org/10.1093/nar/gkab438

Title: DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal
Author: Zheng, Shuyu; Aldahdooh, Jehad; Shadbahr, Tolou; Wang, Yinyin; Aldahdooh, Dalal; Bao, Jie; Wang, Wenyu; Tang, Jing
Contributor: University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
University of Helsinki, Research Program in Systems Oncology
Date: 2021-07-02
Language: eng
Number of pages: 11
Belongs to series: Nucleic Acids Research
ISSN: 0305-1048
URI: http://hdl.handle.net/10138/333475
Abstract: Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.
Subject: SYNERGY
1182 Biochemistry, cell and molecular biology
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