I-Corps: Data2Discovery: DataHub Platform for Drug Safety Analysis

药物安全的一个关键障碍是无法以综合方式利用公共数据资源来充分了解药物和化合物对生物系统的作用。有必要整合与化合物、药物、靶标、基因、疾病、临床试验和已知药物副作用有关的异质数据集，并开发有效的网络数据分析技术来识别或预测重要的生物关系。综合的和相关的信息可用于支持药物开发、药物副作用评估以及相关科学研究和评估的实践。这项拟议的工作还可以用于分析患者的支付模式，预测他们未来账单的支付能力，并通过分析患者的监测数据来推荐更好的生活方式。这可以节省搜索和分析数据的人工成本，避免人工产生的错误，并使医疗预算专注于为社会带来更好的医疗保健的严峻问题。提出的技术(Data2Discovery数据中心平台)使用语义集成和搜索技术来整合与药物安全相关的孤立数据源，使搜索能够发现和解释使用其他方法很难或不可能找到的关联。该团队将寻求将以下工具或方法商业化：1)数据中心集成：将与药物安全有关的数据源整合到一个图形数据库中，并将不同数据集中的相关实体连接起来；2)数据中心浏览器：允许用户浏览不同数据集中的数据/实体；以及3)数据中心预测器：基于预定义的路径模式和生物相似性预测语义关联。这些工具可用于帮助领域专家生成假设，并帮助最终用户了解他们正在服用的药物的副作用。这些技术有可能彻底改变从医疗保健、生命科学和商业分析等领域的数据中获取知识的方式，这些领域的重要数据集是大型、复杂和不同种类的。

A critical barrier in drug safety is the inability to utilize public data resources in an integrated fashion to fully understand the actions of drugs and chemical compounds on biological systems. There is a need to integrate the heterogeneous datasets pertaining to compounds, drugs, targets, genes, diseases, clinical trials, and known drug side effect, and to develop effective network data analytical techniques to identify or predict important biological relationships. The integrated and associated information can be used to support practices in drug development, evaluation of drug side effects, and related scientific research and assessment. The proposed work can also be applied to analyze patient payment patterns and predict their paying capability for coming bills, and recommend better life-style by analyzing monitoring data from patients. This can save cost of manual labor for searching and analyzing data, and avoid errors generated by manual labor, and allows healthcare budget focus on stringent issues of bringing better healthcare for the society.The proposed technology (Data2Discovery DataHub Platform) uses semantic integration and searching technologies to integrate siloed data sources related to drug safety and enables search to find and interpret associations which are hard or impossible to find using other methods. The team will seek to commercialize the following tools or approaches: 1) DataHub Integration: integrating data sources related to drug safety into a graph database and connecting related entities across different datasets; 2) DataHub Browser: allowing users to browse data/entities across different datasets; and 3) DataHub Predictor: predicting semantic association based on pre-defined path patterns and biological similarities. These tools can be used to facilitate domain experts to generate hypotheses, and end users to understand side effects of drugs that they are taking. These technologies have the potential to revolutionize how knowledge is derived from data in domains where the important datasets are large, complex and heterogeneous, such as healthcare, life science and business analytics.