I-Corps: ExpressionBlast

收集高通量数据的新实验方法正在彻底改变生物学和临床研究，现在通常用于临床前药物发现。一些大型的公共和专有数据库收集了这些类型的数据，但是这些数据很大程度上是非结构化的，很难利用。该计划是作为针对制药公司、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开发的，允许他们利用来自公共存储库的大量特定条件数据，并将其与专有的内部数据集成。该框架自动下载、解析和注释来自不同存储库的数据，并辅以易于使用的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功能，以及额外的高级分析选项。这为现有的实验结果提供了新的验证，也为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，因此能够轻松地进行跨物种比较可以减少药物开发的成本和时间。提出的技术解决了处理非结构化数据中的异构性和将来自不同来源的大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的功能（生物学和临床前数据）。该团队的目标是创建一个系统，该系统可以管理多个方面的异构性，并提供垂直集成，使数据能够在许多级别上进行搜索和比较。这种整合是通过使用计算文本挖掘和机器学习方法实现的，这些方法能够从自由文本中获得高质量的信息，以便对大量数据进行自动分类和注释。这项工作还为整合新的基因组数据分析工具提供了一种整体方法，提供了标准服务和基准，可以显着缩短开发时间并提高使用率。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重新利用旧药物。此外，大型医疗中心开始为个体患者收集基因组学和遗传学数据，旨在为每个个体提供量身定制的个性化医疗。将单个患者的结果与大量患者及其临床记录进行比较的能力是找到更适合该患者的治疗方法的关键，从而减少住院时间和并发症。最后，这里创建的软件和方法旨在为任何科学从个人实验室实践转移到共享的全球合作系统中。如果成功部署，这项技术有可能对整个医疗保健行业产生重大影响。

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.