Semantic LAMHDI: Linking diseases to model organism resources

描述(由申请人提供)：我们对人类疾病的理解是通过使用模型系统来研究疾病机制和治疗方法而获得的。由于识别疾病模型是如此关键，已经做出了重大努力，通过LAMHDI(将动物模型与人类疾病门户网站(www.lamhdi.org)相联系的倡议)中的基因正交学和路径链接，将有关疾病和模型生物的信息汇集在一起，从而将研究人员与潜在的疾病模型联系起来。然而，LAMHDI目前是不完整的，因为还没有包括体外生物模型，并且不能基于与人类疾病的表型相似性来识别模型系统。此外，没有机制对疾病及其模型之间的无数关系进行优先排序，这使得识别候选模型和发现新关系都很困难。这项工作的目标是促进疾病研究模型的确定，更好地利用现有的模型生物和体外资源及其相关数据，并提供揭示疾病、表型和基因之间新关系的能力，这将加深我们对疾病的理解。为此，我们建议：1)使用来自人类和模式生物的导入和排列的表型数据，基于表型的语义相似性来计算候选疾病模型。我们将包括表达数据，以基于特定解剖位置和/或基因中表达的存在来改进表型搜索。2)扩大LAMHDI内疾病和体外模型系统之间的语义联系，包括Eagle-I项目和外部来源的生物标本和细胞系等资源。这将使研究人员能够根据表型或遗传基础确定候选的体外模型系统。3)创建一个发现工具来改进搜索，并使用遗传、途径和表型关系来发现疾病、模式生物和体外资源之间的新关系。为了将所需的不同数据汇集在一起，我们利用了语义网技术和复杂的信息建模，并结合了计算算法。可用性研究将为新的以知识为导向的发现LAMDHI界面的迭代开发提供信息。 公共卫生相关性(由申请人提供)：这里提出的这项工作将有助于确定疾病研究的模型系统。这将通过将来自许多不同生物体、体外资源和疾病的数据集中在一个中央门户网站来实现。我们相信，这个系统不仅将更好地利用我们现有的生物体和资源以及有关它们的数据，而且它还将提供发现有关疾病的新信息的能力，这些信息将进一步加深我们对疾病机制和潜在治疗方法的理解。

DESCRIPTION (provided by applicant): Our understanding of human disease is vitally informed by the use of model systems to investigate disease mechanisms and therapies. Because identifying disease models is so critical, a significant effort has been made to connect researchers to potential models of disease by bringing together information about diseases and model organisms via gene orthology and pathway links in LAMHDI, the initiative to Link Animal Models to Human Disease portal (www.lamhdi.org). However, LAMHDI is currently incomplete in that in vitro biological models are not yet included and one cannot identify model systems based on phenotype similarity to a human disease. Furthermore, there is no mechanism by which to prioritize the myriad of relationships between disease and their models, making both identification of candidate models and discovery of new relationships difficult. The goal of this work is to facilitate the identification of models for disease research, make better use of existin model organisms and in vitro resources and data about them, and provide the ability to uncover new relationships between disease, phenotypes and genes that will further our understanding of disease. To this end we propose to: 1) Enable computation of candidate disease models based on semantic similarity of phenotypes using imported and aligned phenotype data from humans and model organisms. We will include expression data to refine search of phenotypes based on presence of expression within a particular anatomical location and/or genotype. 2) Expand semantic linkage between diseases and in vitro model systems within LAMHDI, including resources such as biospecimens and cell lines from the eagle-i project and external sources. This will permit investigators to identify candidate in vitro model systems based on phenotype or genetic basis. 3) Create a discovery tool to refine searches and to uncover novel relationships between diseases, model organisms, and in vitro resources using genetic, pathway, and phenotype relationships. To bring together the disparate data required, we leverage semantic web technologies and sophisticated information modeling combined with computational algorithms. Usability studies will inform the iterative development of the new knowledge-guided discovery LAMDHI interface. PUBLIC HEALTH RELEVANCE (provided by applicant): This work proposed here will facilitate the identification of model systems for disease research. This will be accomplished by bringing together data from many different organisms, in vitro resources, and diseases in one central web portal. We believe that this system will not only make better use of our existing organisms and resources and data about them, but it will provide the ability to uncover new information about disease that will further our understanding of disease mechanisms and potential therapies.