Federation of Brain Data through Knowledge-guided Mediation

Martone, Maryann E.; Gupta, Amarnath; Ludäscher, Bertram; Zaslavsky, Ilya; Ellisman, Mark H.

doi:10.1007/978-1-4615-1079-6_19

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…For example, the values computed for the Lef1 gene dataset demonstrate that it is highly expressed in superior colliculus and several structures in lateral and ventral thalamus such as dorsal lateral geniculate nucleus (DLG), ventral lateral geniculate nucleus (VLG), lateral nuclear group (LNG), and VNG (Figure 8 ). Association between the ROI object and the anatomical structures relates the ROI objects to several levels of the anatomical hierarchy, thus allowing for the extension of data retrieval and comparison at multiple anatomical scales (Boline et al, 2006 ; Martone et al, 2002 ).…”

Section: Resultsmentioning

confidence: 99%

A high-resolution anatomical framework of the neonatal mouse brain for managing gene expression data

Lee¹,

Boline

Toga

2007

Front. Neuroinform.

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This study aims to provide a high-resolution atlas and use it as an anatomical framework to localize the gene expression data for mouse brain on postnatal day 0 (P0). A color Nissl-stained volume with a resolution of 13.3 × 50 × 13.3 μ3 was constructed and co-registered to a standard anatomical space defined by an averaged geometry of C57BL/6J P0 mouse brains. A 145 anatomical structures were delineated based on the histological images. Anatomical relationships of delineated structures were established based on the hierarchical relations defined in the atlas of adult mouse brain (MacKenzie-Graham et al., 2004) so the P0 atlas can be related to the database associated with the adult atlas. The co-registered multimodal atlas as well as the original anatomical delineations is available for download at http://www.loni.ucla.edu/Atlases/. The region-specific anatomical framework based on the neonatal atlas allows for the analysis of gene activity within a high-resolution anatomical space at an early developmental stage. We demonstrated the potential application of this framework by incorporating gene expression data generated using in situ hybridization to the atlas space. By normalizing the gene expression patterns revealed by different images, experimental results from separate studies can be compared and summarized in an anatomical context. Co-displaying multiple registered datasets in the atlas space allows for 3D reconstruction of the co-expression patterns of the different genes in the atlas space, hence providing better insight into the relationship between the differentiated distribution pattern of gene products and specific anatomical systems.

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Section: Resultsmentioning

confidence: 99%

A high-resolution anatomical framework of the neonatal mouse brain for managing gene expression data

Lee¹,

Boline

Toga

2007

Front. Neuroinform.

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“…Because of these realities, there is little immediate prospect for community consensus to converge around a common ontology that supports highly specific semantic integration. Thus, the strategies for successful data integration so prominently demonstrated by the Biomedical Informatics Research Network (BIRN)'s Knowledge Integration of Neuroscience Data (KIND) mediator, and by the ontology-mediation approach of the Geosciences Network (GEON) (Ludäscher et al, 2003;Martone et al, 2002) are not feasible for archaeology. Data integration efforts must therefore aim for what is more achievable, which is data mapping at a much more abstracted and generalized level.…”

Section: Discussionmentioning

confidence: 99%

A community approach to data integration: Authorship and building meaningful links across diverse archaeological data sets

Kansa

2005

Geosphere

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The ability to link and compare diverse archaeological data sets will catalyze innovative research of great scope and analytic rigor. However, information heterogeneity and limited budgets and information technology skills challenge data dissemination initiatives. This paper argues for new methods of community-based data integration pioneered by the University of Chicago's Extensible Markup Language (XML) System for Textual and Archaeological Research project (XSTAR). With XSTAR, data integration takes place in two steps: (1) syntactic-schematic integration: Legacy data sets are migrated for representation in the data structures described by the Archaeological Markup Language (ArchaeoML), and (2) Semantic integration: Mappings must be established between related terms and classes in each source database. Because the nuances of meaning are often very subtle, human experts must classify related items in each data set. Initial syntacticschematic mapping of data into XSTAR is simple and fast but occurs at a relatively abstract level of meaning. Nevertheless, this initial step can accommodate diverse archaeological (and other) data sets, and will facilitate community-led development of more semantically specific data integration. XSTAR hopes to enable multiple semantic data integration schemas to develop and keep pace with changing research agendas. though rooted in archaeology, this paper discusses challenges faced by many disciplines in encouraging more powerful diachronic and regional syntheses. ArchaeoML's highly generalized data model has applicability outside archaeology, especially with subdisciplines of the earth sciences yet to develop formal ontologies. In addition, because this is a community driven approach, incentives for community participation must be explored. Intellectual property and professional rewards are key factors in determining the success of online dissemination systems across many disciplines.

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“…Non-spatial source registration, grid management, mediation and integrated view formulation, and ontology management issues within BIRN, have been addressed elsewhere [eg. 1,5]. In this paper, we explore principles and techniques that enable spatial data interoperability, including spatial registration, discovery, query, and visualization across brain data sources.…”

Section: Introductionmentioning

confidence: 99%

Integrating brain data spatially: spatial data infrastructure and atlas environment for online federation and analysis of brain images

Zaslavsky¹,

He²,

Tran³

et al. 2004

Proceedings. 15th International Workshop on Database and Expert Systems Applications, 2004.

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Federation of Brain Data through Knowledge-guided Mediation

Cited by 5 publications

References 20 publications

A high-resolution anatomical framework of the neonatal mouse brain for managing gene expression data

A high-resolution anatomical framework of the neonatal mouse brain for managing gene expression data

A community approach to data integration: Authorship and building meaningful links across diverse archaeological data sets

Integrating brain data spatially: spatial data infrastructure and atlas environment for online federation and analysis of brain images

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