I-Corps: ExpressionBlast

I军团：ExpressionBlast

• 批准号: 1242525
• 负责人: Ziv Bar-Joseph
• 金额: $ 5万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1242525&HistoricalAwards=false
• 关键词: Corps; ExpressionBlast

New experimental methods for collecting high throughput data are revolutionizing biology and clinical studies and are now routinely used for pre-clinical drug discovery. Several large public and proprietary databases collect these types of data, however this data is largely unstructured and is difficult to utilize. The proposed effort is developed as a search engine for genomic data aimed at pharmaceutical companies, biotechnology companies, academic institutes, and medical centers allowing them to utilize large volumes of condition-specific data from public repositories, and also integrate it with proprietary, in-house data. The framework automatically downloads, parses, and annotates data from different repositories and is complemented with an easy-to-use web interface. The team created a large collection of automatically-annotated data and the platform offers search capabilities within and across species as well as additional advanced analysis options. These can provide new validation for current experimental results as well as new research directions. In particular, drug discovery is usually conducted on lower mammals before it is applied to human, hence the ability to easily perform cross species comparisons can reduce drug development cost and time. The proposed technology addresses the classic problem of dealing with heterogeneity in unstructured data and integrating massive amounts of data from different sources into a seamless framework. The unique challenge here is a function of the domain (biological and pre-clinical data). The team's goal is to create a system that manages heterogeneity in more than a single aspect and provides vertical integration that allows the data to be searchable and comparable on many levels. This integration is made possible through the use of computational text mining and machine learning methods that are able to derive high quality information from the free text in order to automatically categorize and annotate the large volumes of data. This work also provides a holistic approach for the incorporation of new analysis tools for genomic data, offering standard services and benchmarks that can significantly shorten development time and increase usage. The ability to easily query large volumes of genomic data can facilitate basic research of cell processes by academic researchers and the discovery of new drugs or repurposing of old drugs by pharmaceutical companies. In addition, large medical centers are starting to collect genomics and genetics data for individual patients aiming to provide personalized medicine tailored specifically to each individual. The ability to compare results of an individual patient to a large collection of patients and their clinical records is a key to finding better suited treatments for that individual leading to reduced hospitalization time and fewer complications. Lastly, the software and methods created here are intended to be reusable for any science moving from individual lab practices to a shared, global collaboratory system. If successfully deployed, this technology has the potential to make a significant impact across a wide span of the health care industry.

收集高通量数据的新实验方法正在给生物学和临床研究带来革命性的变化，现在被常规用于临床前药物发现。几个大型公共和专有数据库收集这些类型的数据，但这些数据基本上是非结构化的，很难利用。拟议的努力被开发为针对制药公司、生物技术公司、学术机构和医疗中心的基因组数据搜索引擎，允许他们利用公共存储库中的大量特定条件数据，并将其与专有的内部数据相结合。该框架自动下载、解析和注释来自不同存储库的数据，并辅之以易于使用的Web界面。该团队创建了大量自动注释的数据，该平台提供了物种内部和跨物种的搜索能力，以及额外的高级分析选项。这为当前的实验结果提供了新的验证，也为今后的研究提供了新的方向。特别是，药物发现通常在应用于人类之前在低等哺乳动物身上进行，因此容易进行跨物种比较的能力可以减少药物开发成本和时间。提出的技术解决了处理非结构化数据中的异构性以及将来自不同来源的海量数据集成到一个无缝框架中的经典问题。这里的独特挑战是领域(生物学和临床前数据)的功能。该团队的目标是创建一个在多个方面管理异构性的系统，并提供垂直集成，使数据能够在多个级别上进行搜索和比较。这种集成是通过使用计算文本挖掘和机器学习方法来实现的，这些方法能够从自由文本中获得高质量的信息，以便自动地对大量数据进行分类和注释。这项工作还为纳入新的基因组数据分析工具提供了一种全面的方法，提供了可显著缩短开发时间和增加使用量的标准服务和基准。轻松查询大量基因组数据的能力可以促进学术研究人员对细胞过程的基础研究，以及制药公司发现新药或改变旧药的用途。此外，大型医疗中心开始收集个体患者的基因组学和遗传学数据，旨在提供针对每个个体的个性化药物。能够将单个患者的结果与大量患者及其临床记录进行比较，是找到更适合该患者的治疗方法的关键，从而缩短住院时间和减少并发症。最后，这里创建的软件和方法旨在为任何从单个实验室实践到共享的全球协作系统的科学提供可重复使用的能力。如果成功部署，这项技术有可能对医疗保健行业的广泛领域产生重大影响。