EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2022-10-01 DOI:10.1186/s13040-022-00307-9
David Dora, Timea Dora, Gabor Szegvari, Csongor Gerdán, Zoltan Lohinai
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Abstract

The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.

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EZCancerTarget:一个开放获取的药物再利用和数据收集工具,用于加强靶向验证和优化针对高度进展性癌症的国际研究工作。
潜在治疗靶点的不断扩大需要易于获取、结构化和透明的分子数据实时解释。开放获取的基因组学、蛋白质组学和药物再利用数据库改变了癌症研究的格局,但其中大多数对普通用户来说都是困难和耗时的。此外,为了对多个目标进行系统的搜索和数据检索,研究人员需要专业的生物信息学家的帮助,而小型研究团队并不总是可以随时获得这些专家。我们邀请研究小组加入,旨在加强更有经验的团体的合作工作,以协调国际努力,克服毁灭性的恶性肿瘤。在这里,我们整合了现有的基础数据,并提出了一个新颖的,开放获取的,数据聚合的,药物再利用的平台,从Clue.io的条目中得出我们的搜索。我们展示了我们如何整合我们之前在小细胞肺癌(SCLC)方面的专业知识,以启动一个新的平台,以克服高度进展的癌症,如三阴性乳腺癌和胰腺癌。通过前端,平台的现有内容可以进一步扩展或替换,用户可以创建自己的药物靶点列表,选择临床最相关的靶点进行进一步的功能验证分析或药物试验。EZCancerTarget整合了来自公共数据库的搜索,如PubChem, DrugBank, PubMed和EMA,引用每个目标的最新和相关文献。此外,利用UniProt、String和GeneCards等实体,对化合物的信息进行了生物背景信息的补充,展示了这些分子的相关途径、分子和生物学功能以及亚细胞定位。癌症药物的发现需要复杂的,往往是不同领域的融合。我们提出了一个简单、透明、用户友好的药物再利用软件,以促进研究小组在癌症研究领域的努力。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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