SenseLab: new developments in disseminating neuroscience information

Crasto, Chiquito J.; Marenco, Luis N.; Liu, Nian; Morse, Thomas M.; Cheung, Kei‐Hoi; Lai, Peter; Bahl, Gautam; Masiar, Peter; Lam, Hugo Y. K.; Lim, Ernest; Chen, Huajun; Nadkarni, Prakash M.; Migliore, Michele; Miller, Perry L.; Shepherd, Gordon M.

doi:10.1093/bib/bbm018

Cited by 30 publications

(15 citation statements)

References 56 publications

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“…Although an existing cell type ontology was available (OBO Cell Ontology—http://www.obofoundry.org/cgi-bin/detail.cgi?id=cell), the coverage of nerve cells was minimal and insufficient for the neuroscience community. To assemble a more comprehensive list of nerve cells, a compendium of types was pooled from the SenseLab curated nerve cell physiology NeuronDB repository (Crasto et al 2007), the Neuromorpho.org cell morphological model repository (Ascoli et al 2007), and the Cell-Centered Database (CCDB) nerve cell types derived from the associated Subcellular Anatomy Ontology (Martone et al 2008; Larson et al 2007). These types were pooled within a spreadsheet that also listed cell body anatomical locations, released transmitters, circuit types, and other cellular properties.…”

Section: Resultsmentioning

confidence: 99%

The NIFSTD and BIRNLex Vocabularies: Building Comprehensive Ontologies for Neuroscience

et al. 2008

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A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neurosciencerelevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically Neuroinform

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Section: Resultsmentioning

confidence: 99%

The NIFSTD and BIRNLex Vocabularies: Building Comprehensive Ontologies for Neuroscience

et al. 2008

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“…Through the incorporation of clear definitions of core medical concepts and authoritative descriptions into the ontology structure, medical knowledge graphs represent more convenient and efficient multi-sourced heterogeneous data integration and verification, semantic web development, clinical data tagging, storing, retrieval, and aggregation of medical data. Yale University has integrated the neuroscience knowledge base SenseLab to construct the knowledge graph of brain science, from microscopic molecular to macroscopic behavioral levels [7]. It could help computers understand and express the correlation of massive information in neuroscience.…”

Section: Construction Of Medical Knowledge Graph and Terminology Stanmentioning

confidence: 99%

AI Assisted Clinical Diagnosis Treatment, and Development Strategy

Kong¹,

He²,

Li³

2018

Chinese Journal of Engineering Science

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The integration of healthcare data, open access to healthcare data, and use of artificial intelligence to organize and analyze fragmented medical information can improve medical and health services, promote the level of rational decision-making by the government, and reduce any inequality in the allocation of medical and health resources. This paper summarizes the current status of artificial intelligence technologies and their applications in the fields of medical information semantic fusion and image analysis. This study also analyzes the current problems and challenges associated with these technologies. First, the construction of clinical terminology standards by standardized representation and structural integration of medical information is key. Second, using medical knowledge to construct an intelligent diagnosis and treatment model with the capability to combine multimodal data analysis and structured knowledge reasoning is currently the focus of development in image intelligence. Thus, we propose a nationwide healthcare open data cloud platform that can open new data markets, improve the integration of healthcare data, and provide new services pertaining to knowledge discovery and utilization. We also suggest some basic industry standards for medical and health information to strengthen the research and development of domestic medical devices, promote the development of intelligent medical devices and smart wearable devices, and guide the industry toward new frontiers in the combination of artificial intelligence and medical devices.

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“…References to quantitative results in the literature would then allow the implementation of new models and simulations. For example, the BrainPharm database, currently under development to support research on drugs for the treatment of different neurological disorders [18], reports on agents that act on neuronal receptors and signal transduction pathways in the normal brain and in nervous disorders [19]. [5][6] ) mechanisms.…”

Section: Drug Databasesmentioning

confidence: 99%

Computational Models of Neuronal Biophysics and the Characterization of Potential Neuropharmacological Targets

Ferrante¹,

Blackwell²,

Migliore³

et al. 2008

CMC

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The identification and characterization of potential pharmacological targets in neurology and psychiatry is a fundamental problem at the intersection between medicinal chemistry and the neurosciences. Exciting new techniques in proteomics and genomics have fostered rapid progress, opening numerous questions as to the functional consequences of ligand binding at the systems level. Psycho- and neuro-active drugs typically work in nerve cells by affecting one or more aspects of electrophysiological activity. Thus, an integrated understanding of neuropharmacological agents requires bridging the gap between their molecular mechanisms and the biophysical determinants of neuronal function. Computational neuroscience and bioinformatics can play a major role in this functional connection. Robust quantitative models exist describing all major active membrane properties under endogenous and exogenous chemical control. These include voltage-dependent ionic channels (sodium, potassium, calcium, etc.), synaptic receptor channels (e.g. glutamatergic, GABAergic, cholinergic), and G protein coupled signaling pathways (protein kinases, phosphatases, and other enzymatic cascades). This brief review of neuromolecular medicine from the computational perspective provides compelling examples of how simulations can elucidate, explain, and predict the effect of chemical agonists, antagonists, and modulators in the nervous system.

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SenseLab: new developments in disseminating neuroscience information

Cited by 30 publications

References 56 publications

The NIFSTD and BIRNLex Vocabularies: Building Comprehensive Ontologies for Neuroscience

The NIFSTD and BIRNLex Vocabularies: Building Comprehensive Ontologies for Neuroscience

AI Assisted Clinical Diagnosis Treatment, and Development Strategy

Computational Models of Neuronal Biophysics and the Characterization of Potential Neuropharmacological Targets

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