Big Data Training for Cancer Research

癌症研究大数据培训

• 批准号: 10880158
• 负责人: MIN ZHANG
• 金额: $ 22.43万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10880158
• 关键词: Acceleration; Address; Basic Science; Big Data; Big Data to Knowledge; Bioinformatics; Biological; Biology; Cancer Biology; Cancer Center; Case Study; Cause of Death; Clinical; Clinical Cancer Center; Clinical Oncology; Clinical Research; Collaborations; Collection; Communication; Communities; Complement; Complex; Computers; Computing Methodologies; Country; Data; Data Analyses; Data Set; Development; Disease; Educational Curriculum; Funding; Genomics; Goals; Image; Indiana; Infrastructure; Instruction; Intervention; Knowledge; Malignant Neoplasms; Medical; Modification; Molecular; NCI Center for Cancer Research; NCI-Designated Cancer Center; Needs Assessment; Oncology; Participant; Positioning Attribute; Proteomics; Public Domains; Published Comment; Research; Research Personnel; Science; Scientist; Source; Statistical Methods; Surveys; Survival Analysis; Technical Expertise; The Cancer Genome Atlas; Training; Training Programs; Translating; Translations; United States National Institutes of Health; Universities; Visualization; Work; anticancer research; bench to bedside; big-data science; biomarker identification; cancer type; clinical practice; computer science; computerized tools; data resource; data to knowledge; data tools; density; design; disorder risk; education resources; experience; high throughput technology; improved; interoperability; knowledge translation; large datasets; large scale data; metabolomics; multidisciplinary; neoplasm resource; novel therapeutics; precision oncology; programs; risk prediction; skills; statistics; success; tool; transcriptomics; virtual machine; web site

PROJECT SUMMARY The increasing volume of big data in cancer research has the potential to dramatically accelerate the translation of knowledge from bench to bedside. Unfortunately, most cancer researchers are unable to: (i) utilize the valuable big data that is readily available in the public domain, and (ii) extract knowledge from cancer big data through communicating with computer scientists, statisticians and bioinformaticians. Traditionally, cancer researchers are trained in the biologically related sciences that are relevant to the manifestation of the disease. This knowledge is, and remains, critical for understanding the biological and molecular mechanisms that result in the disease and that can be targeted for clinical intervention. However, historically, cancer researchers have not been trained to handle large volumes of data. There was no need; there were not many approaches that were generating large scale data. Yet, with the advent of high-throughput approaches, in particular those related to genomics, proteomics and metabolomics, a significant gap in the training of cancer researchers has become apparent – the need for skills in computer science and statistics to analyze big data and interpret results from the analyses. In the absence of quantitative training for cancer researchers, a bottleneck will remain in the translation of the large body of cancer big data to clinical practice. This need was confirmed in a needs assessment of researchers from 95 Cancer Centers sent out last year (including all 69 NCI-Designated Cancer Centers). To address the need for a big data training course, the investigators propose to build on a previously NIH-funded big data training course, to develop and deliver a new training course tailored to cancer researchers across the country. In a partnership between the Purdue University Center for Cancer Research (PCCR), the Indiana University Simon Cancer Center (IUSCC), and a group of traditionally trained biostatisticians, the team is in a unique position to leverage basic and clinical cancer centers (the only two NCI-Designated Cancer Centers in the State), to work together on this multi-disciplinary training program. In contrast to the previous successful big data training course designed for general biomedical researchers who were novices in big data science, this new course will target cancer researchers with the knowledge of big data value but lacking the quantitative skills necessary to work with it. Based on case studies from both PCCR and IUSCC researchers, the goal of the course is to help participants develop skills for managing, visualizing, analyzing, and integrating various types of cancer big data that are publicly available. This is increasingly important as more and more precision oncology- focused treatments are coming on line. With this customized big data training, cancer researchers can realize the transformative potential of big data by translating it from bench to bedside.

项目摘要 癌症研究中越来越多的大数据有可能大大加速转化 从实验室到病床的知识不幸的是，大多数癌症研究人员无法：（i）利用有价值的 公共领域中随时可用的大数据，以及（ii）通过以下方式从癌症大数据中提取知识 与计算机科学家、统计学家和生物信息学家交流。传统上，癌症研究人员 接受与疾病表现相关的生物相关科学的培训。这 知识是，并仍然是，理解生物和分子机制，导致在关键 这是一种疾病，可以用于临床干预。然而，从历史上看，癌症研究人员没有 接受过处理大量数据的训练。没有必要;没有太多的方法， 产生大规模数据。然而，随着高通量方法的出现，特别是那些与 基因组学，蛋白质组学和代谢组学，在癌症研究人员的培训显着差距已成为 显然-需要计算机科学和统计学技能来分析大数据并解释 的分析。在缺乏对癌症研究人员的定量培训的情况下， 将大量癌症大数据转化为临床实践。这一需求在一个需求中得到了证实。 来自95个癌症中心的研究人员去年发出的评估（包括所有69个NCI-Designated Cancer 中心）。 为了满足大数据培训课程的需求，研究人员建议建立在以前NIH资助的 大数据培训课程，开发和提供为癌症研究人员量身定制的新培训课程， 国家在普渡大学癌症研究中心（PCCR）、印第安纳州 西蒙大学癌症中心（IUSCC）和一组传统的生物统计学家，该小组是在一个 利用基础和临床癌症中心的独特地位（美国仅有的两个NCI指定的癌症中心） 国家），共同努力，在这个多学科的培训计划。与以往成功的大 数据培训课程专为一般生物医学研究人员谁是新手在大数据科学，这个新的 该课程将针对具有大数据价值知识但缺乏定量技能的癌症研究人员 根据PCCR和IUSCC研究人员的案例研究， 本课程旨在帮助参与者发展管理、可视化、分析和整合各种类型的技能。 of cancer癌症big大data数据that are publicly公开available可用.这一点越来越重要，因为越来越精确的肿瘤学- 集中治疗即将上线通过这种定制的大数据培训，癌症研究人员可以实现 大数据的变革潜力，将其从实验室转化为临床应用。