CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer Correlations.

IF 3.4 4区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Database: The Journal of Biological Databases and Curation Pub Date : 2024-07-26 DOI:10.1093/database/baae072
Shahid Ullah, Yingmei Li, Wajeeha Rahman, Farhan Ullah, Muhammad Ijaz, Anees Ullah, Gulzar Ahmad, Hameed Ullah, Tianshun Gao
{"title":"CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer Correlations.","authors":"Shahid Ullah, Yingmei Li, Wajeeha Rahman, Farhan Ullah, Muhammad Ijaz, Anees Ullah, Gulzar Ahmad, Hameed Ullah, Tianshun Gao","doi":"10.1093/database/baae072","DOIUrl":null,"url":null,"abstract":"<p><p>Biological databases serve as critical basics for modern research, and amid the dynamic landscape of biology, the COVID-19 database has emerged as an indispensable resource. The global outbreak of Covid-19, commencing in December 2019, necessitates comprehensive databases to unravel the intricate connections between this novel virus and cancer. Despite existing databases, a crucial need persists for a centralized and accessible method to acquire precise information within the research community. The main aim of the work is to develop a database which has all the COVID-19-related data available in just one click with auto global notifications. This gap is addressed by the meticulously designed COVID-19 Pandemic Database (CO-19 PDB 2.0), positioned as a comprehensive resource for researchers navigating the complexities of COVID-19 and cancer. Between December 2019 and June 2024, the CO-19 PDB 2.0 systematically collected and organized 120 datasets into six distinct categories, each catering to specific functionalities. These categories encompass a chemical structure database, a digital image database, a visualization tool database, a genomic database, a social science database, and a literature database. Functionalities range from image analysis and gene sequence information to data visualization and updates on environmental events. CO-19 PDB 2.0 has the option to choose either the search page for the database or the autonotification page, providing a seamless retrieval of information. The dedicated page introduces six predefined charts, providing insights into crucial criteria such as the number of cases and deaths', country-wise distribution, 'new cases and recovery', and rates of death and recovery. The global impact of COVID-19 on cancer patients has led to extensive collaboration among research institutions, producing numerous articles and computational studies published in international journals. A key feature of this initiative is auto daily notifications for standardized information updates. Users can easily navigate based on different categories or use a direct search option. The study offers up-to-date COVID-19 datasets and global statistics on COVID-19 and cancer, highlighting the top 10 cancers diagnosed in the USA in 2022. Breast and prostate cancers are the most common, representing 30% and 26% of new cases, respectively. The initiative also ensures the removal or replacement of dead links, providing a valuable resource for researchers, healthcare professionals, and individuals. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://www.co-19pdb.habdsk.org/. Database URL: https://www.co-19pdb.habdsk.org/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11281848/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Database: The Journal of Biological Databases and Curation","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/database/baae072","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Biological databases serve as critical basics for modern research, and amid the dynamic landscape of biology, the COVID-19 database has emerged as an indispensable resource. The global outbreak of Covid-19, commencing in December 2019, necessitates comprehensive databases to unravel the intricate connections between this novel virus and cancer. Despite existing databases, a crucial need persists for a centralized and accessible method to acquire precise information within the research community. The main aim of the work is to develop a database which has all the COVID-19-related data available in just one click with auto global notifications. This gap is addressed by the meticulously designed COVID-19 Pandemic Database (CO-19 PDB 2.0), positioned as a comprehensive resource for researchers navigating the complexities of COVID-19 and cancer. Between December 2019 and June 2024, the CO-19 PDB 2.0 systematically collected and organized 120 datasets into six distinct categories, each catering to specific functionalities. These categories encompass a chemical structure database, a digital image database, a visualization tool database, a genomic database, a social science database, and a literature database. Functionalities range from image analysis and gene sequence information to data visualization and updates on environmental events. CO-19 PDB 2.0 has the option to choose either the search page for the database or the autonotification page, providing a seamless retrieval of information. The dedicated page introduces six predefined charts, providing insights into crucial criteria such as the number of cases and deaths', country-wise distribution, 'new cases and recovery', and rates of death and recovery. The global impact of COVID-19 on cancer patients has led to extensive collaboration among research institutions, producing numerous articles and computational studies published in international journals. A key feature of this initiative is auto daily notifications for standardized information updates. Users can easily navigate based on different categories or use a direct search option. The study offers up-to-date COVID-19 datasets and global statistics on COVID-19 and cancer, highlighting the top 10 cancers diagnosed in the USA in 2022. Breast and prostate cancers are the most common, representing 30% and 26% of new cases, respectively. The initiative also ensures the removal or replacement of dead links, providing a valuable resource for researchers, healthcare professionals, and individuals. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://www.co-19pdb.habdsk.org/. Database URL: https://www.co-19pdb.habdsk.org/.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
CO-19 PDB 2.0:具有全局自动预警、统计分析和癌症相关性的 COVID-19 综合数据库。
生物数据库是现代研究的重要基础,在生物学的动态发展中,COVID-19 数据库已成为不可或缺的资源。Covid-19病毒将于2019年12月在全球爆发,因此有必要建立全面的数据库,以揭示这种新型病毒与癌症之间错综复杂的联系。尽管有了现有的数据库,但研究界仍然迫切需要一种集中、易用的方法来获取精确信息。这项工作的主要目的是开发一个数据库,只需点击一下,就能获得所有与 COVID-19 相关的数据,并自动发出全球通知。精心设计的 COVID-19 大流行数据库(COV-19 PDB 2.0)填补了这一空白,该数据库将成为研究人员了解 COVID-19 和癌症复杂性的综合资源。从 2019 年 12 月到 2024 年 6 月,COVID-19 PDB 2.0 系统收集并整理了 120 个数据集,分为六个不同的类别,每个类别都有特定的功能。这些类别包括化学结构数据库、数字图像数据库、可视化工具数据库、基因组数据库、社会科学数据库和文献数据库。功能范围从图像分析和基因序列信息到数据可视化和环境事件更新。CO-19 PDB 2.0 可选择数据库搜索页面或自动识别页面,提供无缝信息检索。专用页面引入了六个预定义图表,提供了对病例和死亡人数、国家分布、"新病例和康复 "以及死亡和康复率等关键标准的深入了解。COVID-19 对癌症患者的全球影响促成了研究机构之间的广泛合作,在国际期刊上发表了大量文章和计算研究。该计划的一个主要特点是每天自动通知标准化信息更新。用户可根据不同类别轻松浏览,或使用直接搜索选项。该研究提供了最新的COVID-19数据集以及有关COVID-19和癌症的全球统计数据,重点介绍了2022年美国诊断出的十大癌症。乳腺癌和前列腺癌最为常见,分别占新病例的30%和26%。该倡议还确保删除或替换死链接,为研究人员、医疗保健专业人员和个人提供宝贵的资源。该数据库采用 PHP、HTML、CSS 和 MySQL 实现,可在 https://www.co-19pdb.habdsk.org/ 免费获取。数据库网址:https://www.co-19pdb.habdsk.org/。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Database: The Journal of Biological Databases and Curation
Database: The Journal of Biological Databases and Curation MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
9.00
自引率
3.40%
发文量
100
审稿时长
>12 weeks
期刊介绍: Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent. The archiving, curation, analysis and interpretation of all of these data are a challenge. Database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data. Database: The Journal of Biological Databases and Curation provides an open access platform for the presentation of novel ideas in database research and biocuration, and aims to help strengthen the bridge between database developers, curators, and users.
期刊最新文献
BuffExDb: web-based tissue-specific gene expression resource for breeding and conservation programmes in Bubalus bubalis. Standardized pipelines support and facilitate integration of diverse datasets at the Rat Genome Database. A change language for ontologies and knowledge graphs. Correction to: The landscape of microRNA interaction annotation: analysis of three rare disorders as a case study. LSD600: the first corpus of biomedical abstracts annotated with lifestyle-disease relations.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1