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 癌症研究中的大数据量的增加有可能显着加速翻译 从长凳到床边的知识。不幸的是，大多数癌症研究人员无法：（i）利用该价值 公共领域中易于获得的大数据，以及（ii）从癌症中提取知识的大数据 与计算机科学家，统计学家和生物信息学家进行交流。传统上，癌症研究人员 接受与疾病表现相关的生物学相关科学培训。这 知识对于理解导致的生物学和分子机制至关重要 疾病，这可以针对临床干预。但是，从历史上看，癌症研究人员尚未 经过培训以处理大量数据。没有必要；没有多少方法 生成大规模数据。但是，随着高通量方法的进步，尤其是与 基因组学，蛋白质组学和代谢组学，癌症研究人员培训的显着差距已成为 显而易见的是 - 计算机科学和统计技能的需求，分析大数据并解释结果 分析。在没有针对癌症研究人员的定量培训的情况下，瓶颈将保留在 将大量癌症的大数据转换为临床实践。这种需求已得到确认 评估去年95个癌症中心的研究人员（包括所有69个NCI指定的癌症 中心）。 为了满足对大数据培训课程的需求，调查人员提议以先前的NIH资助为基础 大数据培训课程，开发并提供针对整个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）之间的合作关系中，印第安纳州 西蒙癌症中心（IUSCC）和一群传统培训的生物统计学家，该团队处于 独特的位置来利用基本和临床癌症中心（仅有两个NCI指定的癌症中心 国家），共同制定这个多学科培训计划。与以前的成功相反 数据培训课程是为大数据科学小说的一般生物医学研究人员设计的，这是新的 课程将以大数据价值了解癌症研究人员，但缺乏定量技能 与之合作的必要条件。基于PCCR和IUSCC研究人员的案例研究，目的 当然是为了帮助参与者发展管理，可视化，分析和整合各种类型的技能 公开可用的癌症大数据。随着越来越精确的肿瘤学 - 专注的治疗即将到来。通过这种定制的大数据培训，癌症研究人员可以意识到 大数据通过将其从长凳转换为床边的变革潜力。