This application seeks funds to develop RegenBase - a novel information system to seamlessly integrate diverse data that are produced by neuroscientists and cell biologists studying nervous system injury, disease and cell motility with other resources, such as the Neuroscience Information Framework and the BioAssay Ontology. Over the past decade the NIH has funded the development of informatics tools and ontologies to allow the integration and interrogation of the massive and diverse data sets that have been produced by the human genome project. In the area of neuroscience the most advancement have been made in the creation and annotation of large anatomical data sets that reveal patterns of gene expression and connectivity. Genesat and the BrainMaps are excellent examples and are easily searched using the Neuroscience Information Framework (NIF) portal. But it is still surprisingly difficult to search for information related to repairing the injured nrvous system. To overcome this road block it is critical to build the essential tools that allow semantic web approaches to link diverse data repositories with ontologies that allow them to be interpreted and analyzed. The success of this initiative critically relies on an effective informatcs solution to integrate the various (current and future) data types generated by neuroscientists working on nervous system injury, as well as large-scale screening efforts (such as the Molecular Libraries Probe Center Network, MLPCN) into coherent data sets and to make them accessible, interpretable, and actionable for scientists of different backgrounds and with different objectives. We propose to develop a novel knowledge-based, extensible information system of interconnected components that leverages semantic-web technologies and domain level ontologies. This system is tentatively called RegenBase (Regeneration dataBase). Tremendous progress has been made during the last decade developing semantic web technologies with the goals of formalizing knowledge, linking information across different domains, and integrating large heterogeneous data sets from diverse sources. To develop RegenBase on a fast-track with limited resources, we will leverage technologies and tools from the National Center for Biological Onotology and the recently launched BioAssay Ontology. The long-term goal of the RegenBase system is seamless """"""""on-the fly"""""""" data integration and analysis via a semantic """"""""Linked Data"""""""" approach that is scalable with respect to information volume and complexity. RegenBase will incorporate biomedical domain-level ontologies, including our recently developed BioAssay Ontology (BAO), to semantically associate related data types and to provide a knowledge context of the underlying experiments and screening outcomes. The overarching goal of this proposed RegenBase system is to allow bench scientists to link data and results from studies on nervous system injury and disease to data and knowledge from other domains with an emphasis on molecular targets and the small molecules that perturb their function to speed the development of novel therapeutics.

Public Health Relevance

Public and private organizations are generating diverse data sets as they attempt to develop therapies for nervous system injury and disease. One reason therapy development is slow lies in the difficulty of collecting, analyzing and displaying information from the thousands of different experiments done on nervous system injury and interpreting it based on knowledge from other areas, such as genomics, cell biology, cancer, immunology and drug discovery. We propose to develop a novel information system that will help neuroscientists working on nerve regeneration to access and use information generated by scientists around the world.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Liu, Yuan
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University of Miami School of Medicine
Schools of Medicine
Coral Gables
United States
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Al-Ali, Hassan; Beckerman, Samuel R; Bixby, John L et al. (2017) In vitro models of axon regeneration. Exp Neurol 287:423-434
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