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

ABSTRACT: An approach towards heterogeneous neuroscience dataset integration is proposed that uses Natural Language Processing (NLP) and a knowledge-based phenotype organizer system (PhenOS) to link ontology-anchored terms to underlying data from each database, and then maps these terms based on a computable model of disease (SNOMED CT(R)). The approach was implemented using sample datasets from fMRIDC, GEO, The Whole Brain Atlas and Neuronames and allowed for complex queries such as "List all disorders with a finding site of brain region X, and then find the semantically related references in all participating databases based on the ontological model of the disease or its anatomical and morphological attributes". Precision of the NLP-derived coding of the unstructured phenotypes in each dataset was

SUBMITTER: Pantazatos SP 

PROVIDER: S-EPMC2874327 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature