Literature DB >> 14992504

Terminological mapping for high throughput comparative biology of phenotypes.

Y A Lussier1, J Li.   

Abstract

Comparative biological studies have led to remarkable biomedical discoveries. While genomic science and technologies are advancing rapidly, our ability to precisely specify a phenotype and compare it to related phenotypes of other organisms remains challenging. This study has examined the systematic use of terminology and knowledge based technologies to enable high-throughput comparative phenomics. More specifically, we measured the accuracy of a multi-strategy automated classification method to bridge the phenotype gap between a phenotypic terminology (MGD: Phenoslim) and a broad-coverage clinical terminology (SNOMED CT). Furthermore, we qualitatively evaluate the additional emerging properties of the combined terminological network for comparative biology and discovery science. According to the gold standard (n = 100), the accuracies (precision / recall) of the composite automated methods were 67% / 97% (mapping for identical concepts) and 85% / 98% (classification). Quantitatively, only 2% of the phenotypic concepts were missing from the clinical terminology, however, qualitatively the gap was larger: conceptual scope, granularity and subtle yet significant, homonymy problems were observed. These results suggest that, as observed in other domains, additional strategies are required for combining terminologies.

Mesh:

Year:  2004        PMID: 14992504      PMCID: PMC2891021          DOI: 10.1142/9789812704856_0020

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  40 in total

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  14 in total

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Authors:  Divya Sardana; Suresh Vasa; Nishanth Vepachedu; Jing Chen; Ranga Chandra Gudivada; Bruce J Aronow; Anil G Jegga
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6.  Homolonto: generating homology relationships by pairwise alignment of ontologies and application to vertebrate anatomy.

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7.  Integration of Neuroimaging and Microarray Datasets through Mapping and Model-Theoretic Semantic Decomposition of Unstructured Phenotypes.

Authors:  Spiro P Pantazatos; Jianrong Li; Paul Pavlidis; Yves A Lussier
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8.  Integration of Neuroimaging and Microarray Datasets through Mapping and Model-Theoretic Semantic Decomposition of Unstructured Phenotypes.

Authors:  Spiro P Pantazatos; Jianrong Li; Paul Pavlidis; Yves A Lussier
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9.  An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits.

Authors:  Yang Liu; Jianrong Li; Lee Sam; Chern-Sing Goh; Mark Gerstein; Yves A Lussier
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10.  PhenoGO: an integrated resource for the multiscale mining of clinical and biological data.

Authors:  Lee T Sam; Eneida A Mendonça; Jianrong Li; Judith Blake; Carol Friedman; Yves A Lussier
Journal:  BMC Bioinformatics       Date:  2009-02-05       Impact factor: 3.169

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