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

I-Corps：Data2Discovery：用于药物安全分析的 DataHub 平台

• 批准号: 1505374
• 负责人: Ying Ding
• 金额: $ 5万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505374&HistoricalAwards=false
• 关键词: Corps; Data2Discovery; DataHub; Platform; Drug

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.

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，以充分了解药物和化合物对生物系统的作用。需要整合与化合物、药物、靶标、基因、疾病、临床试验和已知药物副作用有关的异构数据集，并开发有效的网络数据分析技术以识别或预测重要的生物学关系。整合的相关信息可用于支持药物开发、药物副作用评价以及相关科学研究和评估的实践。 该方法还可以用于分析患者的支付模式，预测患者的未来支付能力，并通过分析患者的监测数据来推荐更好的生活方式。这样可以节省人工搜索和分析数据的成本，避免人工产生的错误，并允许医疗保健预算集中在为社会带来更好的医疗保健的严格问题上。（Data 2Discovery DataHub平台）使用语义集成和搜索技术来集成与药物安全性相关的孤立数据源，并使搜索能够找到和解释难以或不可能的关联使用其他方法来查找。该团队将寻求将以下工具或方法商业化：1）DataHub Integration：将与药物安全相关的数据源集成到图形数据库中，并连接不同数据集之间的相关实体; 2）DataHub Browser：允许用户浏览不同数据集之间的数据/实体; 3）DataHub Predictor：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如何从重要数据集庞大、复杂和异构的领域（如医疗保健、生命科学和业务分析）的数据中获取知识。