Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination.

BACKGROUND: LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. RESULTS: Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG's resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. CONCLUSIONS: The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development.

External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction. We believe such an efficient and secure approach provides a competitive advantage to companies needing access to the latest information for a range of use cases and complements manually curated commercial sources.

ABO and Rh(D) phenotype frequencies of different racial/ethnic groups in the United States.

BACKGROUND: Commonly quoted ABO/Rh(D) frequencies in the US are usually from relatively small studies with racial or ethnic categories often judged by name or appearance. STUDY DESIGN AND METHODS: A 10-year demographic database that contained racial or ethnic and ABO/Rh(D) phenotype data on 3.1 million allogeneic and autologous donors giving blood at five blood centers in the US was used to compute ABO and Rh(D) phenotypes in various racial/ethnic groups. The racial or ethnic category was designated by the donor. RESULTS: The highest percentage of Group O was found in Hispanic (56.5%), North American Indian (54.6%), and black non-Hispanic (50.2%) donors. Hispanic and black non-Hispanic donors had a much lower percentage (7.3 and 7.1%, respectively) of Rh- compared to white non-Hispanic donors (17.3%). Group O Rh- and Group B Rh- were found more commonly (8.0 and 1.8%, respectively) in white non-Hispanic donors than in Hispanic (3.9 and 0.7%), black non-Hispanic (3.6 and 1.3%), and Asian (0.7 and 0.4%) donors. CONCLUSIONS: These data confirmed that the highest percentages of ORh+, BRh+/ABRh+, and Rh- are present in Hispanic, Asian, and white non-Hispanic donors, respectively. These are the largest and most accurate data of ABO/Rh(D) phenotype frequencies for the major racial/ethnic donor groups in the US.