Cancer Informatics Shared Resource

癌症信息学共享资源

• 批准号: 10626515
• 负责人: Colin Noel Dewey
• 金额: $ 14.16万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-04-25 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10626515
• 关键词: Acceleration; Adopted; Adoption; Algorithms; Applications Grants; Area; Award; Bioinformatics; Biological; Biometry; Businesses; Cancer Center; Cancer Center Support Grant; Clinical; Clinical Informatics; Collaborations; Comprehensive Cancer Center; Computational Science; Computing Methodologies; Data; Data Analyses; Dedications; Development; Direct Costs; Discipline of obstetrics; Doctor of Philosophy; Electronic Health Record; Emerging Technologies; Ensure; Epigenetic Process; Faculty; Funding; Funding Agency; Gene Expression; Genome; Grant; Growth; Gynecology; Head and Neck Cancer; High-Throughput Nucleotide Sequencing; Image; Image Analysis; Immune signaling; Individual; Informatics; Instruction; Machine Learning; Malignant Neoplasms; Medical Informatics; Mentors; Methodology; Mission; Modeling; NCI Center for Cancer Research; Natural Language Processing; Pilot Projects; Positioning Attribute; Postdoctoral Fellow; Radionuclide therapy; Research; Research Design; Research Personnel; Research Project Grants; Resources; Role; Scientist; Services; Standardization; System; Technology; Time; United States National Institutes of Health; Universities; Wisconsin; Work; anticancer research; artificial neural network; computational pipelines; computing resources; cost; deep learning; deep neural network; experience; genome annotation; graduate student; heterogenous data; high dimensionality; informatics shared resource; innovation; member; method development; novel; professor; programs; single-cell RNA sequencing; skills; transcription regulatory network; transcriptome sequencing

PROJECT SUMMARY / ABSTRACT The mission of the Cancer Informatics Shared Resource (CISR) is to promote high quality, innovative cancer research by providing members of the University of Wisconsin Carbone Cancer Center (UWCCC) with cutting- edge and efficient informatics, including bioinformatics, clinical informatics, image analysis, machine learning, and computational science. CISR accomplishes this mission through a team of five highly skilled PhD staff scientists, four world-renowned informatics faculty, and the substantial computational systems and support of the University of Wisconsin Department of Biostatistics and Medical Informatics (BMI), within which CISR was established in 2003. Specific Aim 1. Provide State-of-the-art Informatics Services. CISR provides deep and broad expertise in bioinformatics and machine learning analysis of biological, clinical, and imaging data. CISR staff are well versed in the latest computational approaches for a wide variety of tasks and can efficiently implement and apply them to support UWCCC member projects. This support is provided through dedicated effort of CISR staff to members’ funded projects; involvement of CISR staff in study design, pilot studies, and grant application development; and development of standardized computational pipelines. Specific Aim 2. Develop Innovative Informatics Methodology to Support Cancer Research. CISR staff develop novel computational approaches and algorithms to allow UWCCC research to reach its full potential when existing approaches are insufficient. Support for this aim is bolstered by the effort of four faculty members with expertise in the development of novel algorithms in areas such as machine learning, electronic health record analysis, causal discovery from observational data, natural language processing, deep learning, and RNA-Seq gene expression data analysis. Faculty both collaborate with and mentor the PhD scientists in the development of novel computational approaches and their application to members’ research. During the current Cancer Center Support Grant (CCSG) cycle, support for this aim was enhanced with the growth of the staff from three to five scientists, further increasing capacity and expanding the expertise of CISR to include immunological signaling, genome annotation, transcriptional regulatory networks, and epigenetics. During this cycle, CISR supported 61 individual UWCCC members across all six scientific programs and usage doubled over the prior cycle, in terms of the average number of members served per year. With UWCCC investigators, CISR staff co-authored 34 cancer-related articles, contributed to the submission of 30 grant applications, and supported grants totaling $14M in direct costs, including five from the NCI and four from other NIH institutes. CISR has had important roles in several large-scale UWCCC projects, including the Head and Neck Cancer SPORE (P50DE026787) and a P01 for targeted radionuclide therapy (P01CA250972). CCSG funding of CISR will ensure that its team will continue to be well positioned to meet the evolving computational research needs of UWCCC investigators for cutting-edge cancer research, which is increasingly reliant on massive and heterogeneous data sets.

项目摘要/摘要 癌症信息共享资源(CISR)的使命是促进高质量、创新的癌症 研究通过向威斯康星大学碳骨癌中心(UWCCC)的成员提供切割- 边缘和高效信息学，包括生物信息学、临床信息学、图像分析、机器学习、 和计算科学。CSR通过一个由五名高技能博士工作人员组成的团队来完成这项任务 科学家，四位世界知名的信息学教师，以及强大的计算系统和支持 威斯康星大学生物统计和医学信息学系(BMI)，在该系内 成立于2003年。具体目标1.提供最先进的信息学服务。《公约》提供了深入的和 在生物信息学和生物、临床和成像数据的机器学习分析方面拥有广泛的专业知识。《信息与传播技术公约》 工作人员精通各种任务的最新计算方法，并能有效地 实施并应用它们来支持UWCCC成员项目。这种支持是通过专用的 CSR工作人员对成员资助项目的努力；CISR工作人员参与研究设计、试点研究和 资助应用程序开发；以及标准化计算管道的开发。具体目标2. 发展创新的信息学方法论来支持癌症研究。CSR工作人员开发小说 计算方法和算法，使UWCCC研究在存在的情况下充分发挥其潜力 仅有的方法是不够的。对这一目标的支持得到了四名拥有专业知识的教职员工的努力 在机器学习、电子健康记录分析、 从观察数据、自然语言处理、深度学习和RNA-Seq基因中发现因果关系 表情数据分析。教职员工与博士科学家合作并指导他们开发 新的计算方法及其在成员研究中的应用。在目前的癌症中心 在支助补助金(CCSG)周期中，随着工作人员从3人增加到5人，对这一目标的支持得到了加强 科学家们，进一步提高能力并扩大《信息与传播技术公约》的专门知识，将免疫信号纳入其中， 基因组注释、转录调控网络和表观遗传学。在这一周期内，《公约》支持了61 在所有六个科学计划和使用方面，UWCCC的个别成员比上一个周期翻了一番 平均每年服务的会员人数。与UWCCC调查人员共同撰写了34份 与癌症有关的文章，帮助提交了30份赠款申请，并支助了总计 1400万美元的直接成本，其中5笔来自NCI，4笔来自NIH的其他研究所。《公约》发挥了重要作用 在几个大规模的UWCCC项目中，包括头颈部癌症孢子(P50DE026787)和一个 P01为放射性核素靶向治疗(P01CA250972)。CCSG对CISR的资助将确保其团队将 继续处于有利地位，以满足UWCCC调查人员不断变化的计算研究需求 尖端癌症研究，越来越依赖于海量和不同种类的数据集。