Big Data Training for Cancer Research

癌症研究大数据培训

• 批准号: 10249256
• 负责人: MIN ZHANG
• 金额: $ 23.22万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-17 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10249256
• 关键词: 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; Collection; Communities; Complement; Complex; Computers; Computing Methodologies; Country; Custom; 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; Work; anticancer research; base; 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.

项目总结