Data Science Shared Resource

数据科学共享资源

• 批准号: 10478027
• 负责人: Yang Xie
• 金额: $ 21.82万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10478027
• 关键词: Algorithms; Applications Grants; Area; Big Data; Bioinformatics; Bioinformatics Shared Resource; Biological; Biostatistics Shared Resource; Biotechnology; Catchment Area; Cells; Clinical; Clinical Data; Clinical Trials Design; Commuting; Comprehensive Cancer Center; Computer Analysis; Computer Systems; Computer software; Consultations; Custom; Data; Data Analyses; Data Analytics; Data Commons; Data Management Resources; Data Science; Data Set; Databases; Development; Doctor of Philosophy; Education; Educational workshop; Electronic Health Record; Experimental Designs; Faculty; Funding; Generations; Genetic; Genomics; Goals; Grant; High Performance Computing; Human Resources; Image; Informatics; Infrastructure; Journals; Leadership; Malignant Neoplasms; Malignant neoplasm of liver; Malignant neoplasm of lung; Medical center; Methodology; Modernization; Molecular; Molecular Profiling; NCI Center for Cancer Research; Names; Nature; Oncology; Outcome; Peer Review; Population Sciences; Preparation; Principal Investigator; Process; Proteomics; Publications; Publishing; Quality Control; Renal carcinoma; Research; Research Design; Research Personnel; Research Priority; Research Project Grants; Resource Sharing; Resources; Science; Secure; Series; Services; Software Tools; Systems Integration; Technology; Texas; Tissue imaging; Tissues; Training; Training Programs; Work; Yang; algorithm development; analytical tool; anticancer research; artificial intelligence algorithm; bioinformatics tool; biomarker discovery; biomarker validation; cancer imaging; computer infrastructure; computerized tools; cost effective; data exchange; data integration; data management; data science infrastructure; data sharing; digital; electronic data; experience; high throughput screening; informatics infrastructure; informatics tool; innovation; live cell imaging; member; microbiome; next generation sequencing; novel; operation; predictive modeling; programs; research study; risk prediction model; skills; success; tool; user-friendly; web portal; web services

The development of modern high-throughput biotechnologies and the rapid generation of high-complexity biological data has revolutionized the way cancer is studied. Over the last six years, novel artificial intelligence (AI) algorithm developments together with ever-growing big data generation provide unprecedented opportunities for cancer research, but also major challenges for handling, analyzing, sharing, integrating, and interpreting big data. Recognizing the importance of these data, the Data Science Shared Resource (DSSR) of Simmons Comprehensive Cancer Center (SCCC) was established in 2010 under the leadership of Yang Xie, PhD. The goal of the DSSR is to provide comprehensive informatics, data analytics, data integration, and data management support for SCCC investigators. Specifically, the DSSR provides (1) access to high-performance computing systems, (2) support for bioinformatics and data analyses, including high-throughput molecular data pre-processing, quality assessment and analysis, and cancer image data analysis, (3) support for data integration and risk prediction modeling, including integrative analysis, biomarker discovery, and development of prediction models for clinical outcomes, (4) support for data management and data-sharing, including developing comprehensive databases and facilitating investigators’ use of publicly available datasets and bioinformatics tools, (5) data science support for grant applications, and 6) analytical tools/software distribution and education. In addition, the DSSR has developed a series of cancer data commons and web portals for lung, kidney, and liver cancers, which are high-priority research areas in the SCCC catchment area. The DSSR also developed computational tools to curate and integrate data from electronic health records (EHR) with data from SCCC-driven genomic, imaging, and tissue analysis research. The DSSR will continue to develop, maintain, and apply these integrated platforms to support SCCC projects. In the current project period, DSSR services were utilized by 136 investigators across all five SCCC research programs and provided key contributions to support the success of more than 15 NCI-funded research project grants and over 200 peer-reviewed publications, including work published in high-impact journals such as Nature, Science, Cell, JAMA Oncology, Cancer Discovery, Lancet Oncology, and Nature Genetics. DSSR services are made possible by the accumulated experience of the staff and by innovative, unique, and customized approaches to solving data analysis challenges. With strong support from SCCC and highly cost-effective operations, the DSSR provides extensive services for many SCCC members and has provided critical contributions to the scientific needs and objectives of SCCC.

现代高通量生物技术的发展和高复杂性 生物学数据已经彻底改变了癌症研究的方式。在过去的六年里，新的人工智能 (AI)算法的发展以及不断增长的大数据生成提供了前所未有的 癌症研究的机遇，但也面临着处理，分析，共享，整合和 解读大数据认识到这些数据的重要性， 西蒙斯综合癌症中心（SCCC）成立于2010年，由杨燮领导， PhD. DSSR的目标是提供全面的信息学，数据分析，数据集成和数据 为SCCC调查人员提供管理支持。具体而言，DSSR提供（1）访问高性能 计算系统，（2）支持生物信息学和数据分析，包括高通量分子数据 预处理、质量评估和分析以及癌症图像数据分析，（3）数据支持 集成和风险预测建模，包括集成分析、生物标志物发现和开发 临床结果的预测模型，（4）支持数据管理和数据共享，包括 开发综合数据库，便利调查人员使用公开数据集， 生物信息学工具，（5）资助申请的数据科学支持，以及6）分析工具/软件分发 和教育此外，DSSR还开发了一系列肺癌数据共享和门户网站， 肾癌和肝癌，这是SCCC集水区的高优先级研究领域。DSSR还 开发了计算工具，以管理和整合来自电子健康记录（EHR）的数据， SCCC驱动的基因组，成像和组织分析研究。DSSR将继续开发、维护和 应用这些集成平台来支持SCCC项目。在本项目期间， 136名研究人员在所有五个SCCC研究项目中使用，并为支持 超过15个国家癌症研究所资助的研究项目赠款和200多个同行评审的出版物的成功， 包括发表在高影响力期刊上的工作，如Nature，Science，Cell，JAMA Oncology，Cancer Discovery，Lancet Oncology，Nature Genetics. DSSR服务是由积累的 工作人员的经验和创新，独特和定制的方法来解决数据分析 挑战在SCCC的大力支持和高成本效益的运营下，DSSR提供了广泛的 为许多SCCC成员提供服务，并为科学需求和目标做出了重要贡献 关于SCCC