Computational neuroanatomy: ontology-based representation of neural components and connectivity
Autor: | Ion-Florin Talos, Mark A. Musen, Daniel L. Rubin, Michael Halle, Ron Kikinis |
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Jazyk: | angličtina |
Rok vydání: | 2009 |
Předmět: |
Nervous system
Decision support system Computer science Information Storage and Retrieval Ontology (information science) Biochemistry Nervous System 03 medical and health sciences 0302 clinical medicine Structural Biology Human–computer interaction medicine Computer Graphics Translational research informatics 030212 general & internal medicine Molecular Biology 030304 developmental biology 0303 health sciences Artificial neural network business.industry Applied Mathematics Representation (systemics) Computational Biology Computer Science Applications Variety (cybernetics) Neuroanatomy medicine.anatomical_structure Proceedings Artificial intelligence Neural Networks Computer Explicit knowledge business Medical Informatics |
Zdroj: | BMC Bioinformatics |
ISSN: | 1471-2105 |
Popis: | Background A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. Results We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Conclusion Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future. |
Databáze: | OpenAIRE |
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