Integrating cancer datasets for predictive model development and training

整合癌症数据集以进行预测模型开发和训练

• 批准号: 7878894
• 负责人: Stephen Henry Friend
• 金额: $ 133.89万
• 依托单位: SAGE BIONETWORKS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-03 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7878894
• 关键词: Archives; Area; Cancer Biology; Cancer Death Rates; Cardiovascular Diseases; Characteristics; Clinical; Clinical Data; Common Neoplasm; Communities; Complex; Computational Biology; Coupled; DNA; Data; Data Set; Data Sources; Disease; Drug Delivery Systems; Early Diagnosis; Early treatment; Education; Education and Outreach; Environment; Fellowship Program; Fred Hutchinson Cancer Research Center; Functional disorder; Future; Generations; Genetic Transcription; Genomics; Goals; Grant; Individual; Institutes; Link; Malignant Neoplasms; Medical Research; Mentors; Mentorship; Messenger RNA; Meta-Analysis; Methods; Mission; Modeling; Molecular; Netherlands; Normal tissue morphology; Outcome; Patients; Postdoctoral Fellow; Predictive Value; Process; Prospective Studies; Research Personnel; Sampling; Scientist; Series; Site; Source; System; Systems Biology; Technology; Testing; Training; Validation; Variant; Vision; Work; cancer therapy; cohort; experience; human disease; mathematical model; model development; molecular phenotype; network models; oncology; predictive modeling; programs; research study; tool; trait; tumor

DESCRIPTION (provided by applicant): Our proposal for a Sage CCSB, "Integrating cancer datasets for predictive model development and training," has as its central scientific theme the generation of a set of probabilistic causal models for a series of tumor types from numerous collaborators. By selecting sample sets with different clinical outcomes, the resultant Sage models will have applications impacting cancer biology, early intervention, and cancer treatments. The Sage CCSB leverages the extensive work done at Rosetta/Merck on predictive models in numerous disease areas, which has been gifted to a new nonprofit medical research organization, "Sage Bionetworks." The Sage CCSB operational model contains a core platform of curated data, mathematical models and experienced investigators mentoring postdoctoral trainees/fellows. The data comes from collaborators and consists of DNA variation data, RNA expression data and clinical outcomes. The trainees will collate and annotate the genotypic, intermediate molecular phenotype, and clinical end point data from at least five different tumor-type cohorts and develop models that can predict potential new cancer targets, markers for early detection, and clinical outcomes. They will do externships at other sites (CCSBs), where they will build additional models of their data and facilitate reciprocal exchange of ideas. The trainees will delineate specifications for tools that will make the access to these models more scalable. Validation of their hypotheses will be performed at the Fred Hutchinson Cancer Research Center and the Netherlands Cancer Institute. This post-doctoral program will provide a unique training and mentorship environment in cancer systems biology and facilitate interactions between CCSBs and NCI.

描述（由申请人提供）：我们对Sage CCSB的提案，“整合用于预测模型开发和培训的癌症数据集”，其核心科学主题是来自众多合作者的一系列肿瘤类型的一组概率因果模型的生成。通过选择具有不同临床结果的样本集，Sage模型将具有影响癌症生物学、早期干预和癌症治疗的应用。Sage CCSB利用了Rosetta/默克在许多疾病领域的预测模型方面所做的大量工作，这些工作已被捐赠给一个新的非营利性医学研究组织“Sage Bionetworks”。Sage CCSB运营模式包含一个核心平台，由精心策划的数据、数学模型和经验丰富的研究人员指导博士后学员/研究员。这些数据来自合作者，包括DNA变异数据、RNA表达数据和临床结果。学员将整理和注释来自至少五个不同肿瘤类型队列的基因型、中间分子表型和临床终点数据，并开发可以预测潜在新癌症靶点、早期检测标记和临床结果的模型。他们将在其他网站（CCSBs）进行实习，在那里他们将为自己的数据建立额外的模型，并促进思想的相互交流。受训者将描述工具的规范，这些工具将使对这些模型的访问更具可伸缩性。他们的假设将在弗雷德哈钦森癌症研究中心和荷兰癌症研究所进行验证。该博士后项目将为癌症系统生物学提供独特的培训和指导环境，并促进CCSBs与NCI之间的相互作用。