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Methods Inf Med 2005; 44(01): 32-37
DOI: 10.1055/s-0038-1633920
Original Article
Schattauer GmbH

Automated Tissue Analysis – a Bioinformatics Perspective

A. Kriete
1   Icoria, Pittsburgh, PA, USA
2   Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA
,
K. Boyce
1   Icoria, Pittsburgh, PA, USA
3   Immune Tolerance Network, Pittsburgh, PA, USA
› Author Affiliations
Further Information

Publication History

Received: 10 November 2003

accepted 21 April 2004

Publication Date:
06 February 2018 (online)

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Summary

Objectives: Recent progress in automated tissue analysis (tissomics) provides reproducible phenotypical characterization of histological specimens. We introduce informatics tools to cluster and correlate quantitative tissue profiles with gene expression data. The great potential of synergies between tissue analysis and bioinformatics and its perspectives in medical research and computational diagnostics are discussed.

Methods: Key enablers in microscopic imaging and machine vision are reviewed to perform a high-throughput tissue analysis. Methodologies are described and results are demonstrated that support a combined analysis of tissue with gene expression profiles whereby the consideration of individual responses is key.

Results: Comprehensive histomorphometric profiles, extracted using machine vision, provide information regarding the components and heterogeneity of a tissue in a reproducible format amenable to data mining and analysis. Tissue quantitative information can be placed in synergetic context with bioinformatics data, such as gene expression profiles, for a more comprehensive stratification of individual responses. From a bioinformatics point of view tissue data are co-variants that support the identification of candidate genes relevant in tissue injury or disease.

Conclusions: Progress in automated analytics enables the generation of quantitative data about tissue previously limited to visual histopathology. Such reproducible data sets can be statistically correlated and clustered throughout the continuum of bioinformatics. The combined approach supports a system-wide view of biology and has a potential to accelerate developments for a personalized computational diagnosis.

Keywords

Digital tissue imaging - phenotyping - tissomics - multi-sample comparison - computational diagnosis - biomedical informatics - systems biology
 

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