Platform for High-Throughput Analysis of Integrated Cancer Imaging and Multi-Omics Data

综合癌症成像和多组学数据高通量分析平台

• 批准号: 9568920
• 负责人: Patricia Buendia
• 金额: $ 22.38万
• 依托单位: INFOTECH SOFT, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-11 至 2018-06-10
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9568920
• 关键词: Bioinformatics; Biological; Biology; Biometry; Catalogs; Classification; Communities; Complex; Computer software; Computers; Controlled Vocabulary; Correlation Studies; Data; Data Analyses; Data Set; Data Sources; Data Storage and Retrieval; Detection; Dimensions; Documentation; Ensure; Feedback; Gene Chips; Glioblastoma; Goals; Graph; Image; Individual; Ingestion; Malignant Neoplasms; Measurement; Medical Imaging; Metadata; Methods; Modeling; Molecular; Ontology; Pathway interactions; Phase; Phenotype; Process; Proteome; Proteomics; Protocols documentation; Quality Control; Reporting; Research; Retrieval; Sampling; Slice; Software Engineering; Source; Structure; System; Systems Analysis; Techniques; Technology; Testing; The Cancer Genome Atlas; Vocabulary; base; cancer imaging; data format; data integration; data visualization; database schema; design; experimental study; genetic signature; genome sequencing; graphical user interface; high throughput analysis; imaging modality; improved; in vivo imaging; innovative technologies; insight; knowledge base; metabolome; metabolomics; multiple omics; next generation sequencing; precision medicine; product development; prototype; public health research; radiomics; repository; tool; transcriptome; transcriptomics; tumor; user centered design

Cancer studies increasingly include medical imaging and measurements from multiple omics techniques. The main impetus for data integration is that, through these integrated data sets, an improved understanding of the underlying biology is obtained to be better able to predict a phenotype and to gain further insight into mechanistic aspects of the system at the molecular level. In this project we propose to develop innovative technologies to integrate metabolite data with multi-omic (metabolomics, proteomics and transcriptomics) and cancer imaging data to enable the detection of subtler and more complex associations among variables, with the medical imaging and the metabolome providing phenotypic measurements to which we can anchor the global measurements of the transcriptome and proteome. The proposed Multi-omics and Imaging Data Analysis System (MIDAS) will provide for the ingestion, annotation, quality control, and analysis of in vivo imaging data combined with ex vivo -omics data to advance research in cancer. MIDAS is aimed at helping the cancer and overall public health research communities advance faster towards the larger goal of precision medicine through valid and reliable data harmonization of metabolomics, transcriptomics, proteomics, radiomics and other imaging data.

癌症研究越来越多地包括医学成像和多种组学技术的测量。数据集成的主要推动力是，通过这些集成的数据集，可以更好地了解基础生物学，从而能够更好地预测表型并在分子水平上进一步深入了解系统的机制方面。在这个项目中，我们建议开发创新技术，将代谢数据与多组学（代谢组学、蛋白质组学和转录组学）和癌症成像数据相整合，从而能够检测变量之间更微妙、更复杂的关联，并通过医学成像和代谢组提供表型测量，我们可以将转录组和蛋白质组的全局测量锚定在表型测量上。拟议的多组学和成像数据分析系统（MIDAS）将提供体内成像数据与离体组学数据相结合的摄取、注释、质量控制和分析，以推进癌症研究。 MIDAS 旨在通过代谢组学、转录组学、蛋白质组学、放射组学和其他成像数据的有效和可靠的数据协调，帮助癌症和整个公共卫生研究界更快地实现精准医学的更大目标。