Biomedical subjects

P Mork

Publications and source records attributed to P Mork.

8 recordsLinked to original sources

PubMed2005

BioMediator data integration: beyond genomics to neuroscience data.

The BioMediator system developed at the University of Washington (UW) provides a theoretical and practical foundation for data integration across diverse biomedical research domains and various data types. In this paper we demonstrate the generalizability of its architecture through its application to the UW Human Brain Project (HBP) for understanding language organization in the brain. We first describe the system architecture and the characteristics of the four data sources developed by the UW HBP. Second we present the process of developing the application prototype for HBP neuroscience researchers posing queries across these semantically and syntactically heterogeneous neurophysiologic data sources. Then we discuss the benefits and potential limitations of the BioMediator system as a general data integration solution for different user groups in genomic and neuroscience research domains.

Brain↗

PubMed2005

Integration of data for gene annotation using the BioMediator system.

Gene annotation requires integration of data from multiple sources in order to functionally classify genes. We are using BioMediator, a general purpose data-integration solution, to develop a gene annotation system to automate the process of collecting data from disparate genomic databases. Integration of annotation data from multiple sources into a single format will facilitate use of analytic tools for the proper functional classification of genes.

Base Sequence↗

PubMed2003

Expression array annotation using the BioMediator biological data integration system and the BioConductor analytic platform.

This paper presents the implementation of a model for expression array annotation (EAA) using the BioMediator biological data integration system along with BioConductor, an analytic tools platform. The model presented addresses the need for annotation sources identified during BioConductor inverted exclamation mark s development. Annotation provides us with well-curated genomic background knowledge for expression array analysis and interpretation. Annotation requests are constructed and posted to the query interface of the EAA package (the EAA model implemented as a component of BioConductor). The software enumerates all possible annotation paths for queries. These are then transformed to PQL queries and processed by BioMediator. Annotation entities returned from the EAA package answer the annotation request.

Computational Biology↗

PubMed2002

PQL: a declarative query language over dynamic biological schemata.

We introduce the PQL query language (PQL) used in the GeneSeek genetic data integration project. PQL incorporates many features of query languages for semi-structured data. To this we add the ability to express metadata constraints like intended semantics and database curation approach. These constraints guide the dynamic generation of potential query plans. This allows a single query to remain relevant even in the presence of source and mediated schemas that are continually evolving, as is often the case in data integration.

Computational Biology↗

PubMed2002

A rule driven bi-directional translation system for remapping queries and result sets between a mediated schema and heterogeneous data sources.

As the number of online biomedical data sources increases, so too do the number of ways to access such data. The research described herein focuses on creating a data access system that provides bi-directional translation and mapping of data between heterogeneous databases and a mediated schema. Semantic mapping rules stored in a knowledge base are used by our generalized software to convert XML query results obtained from each data source to a common schema representing a single ontology. We apply this approach to the domain of online genetic databases, demonstrating the system's scalability and integratability.

Databases as Topic↗

PubMed2001

A model for data integration systems of biomedical data applied to online genetic databases.

We present a general model for data integration systems using a mediated schema to represent commonalities in the underlying sources. These sources are mapped to the mediated schema using source descriptions. Users can pose queries against the mediated schema, allowing the system to generate automatically a query plan that enumerates and ranks all possible ways in which the query could be answered. We apply this approach to the domain of online genetic databases, demonstrating the system s ability to answer relevant queries across multiple sources.

Databases, Genetic↗

PubMed2000

Guideline classification to assist modeling, authoring, implementation and retrieval.

The National Guideline Clearinghouse (NGC) and its guideline classification system are significant contributions to the study of clinical practice guidelines (CPGs) and their incorporation into routine clinical care. The NGC classification system is primarily designed to support guideline retrieval. We believe that a guideline classification system should also support identification of features that relate to incorporation of executable CPGs into computer-based applications for sharing and delivering guideline-based advice. We have developed a proposed expansion of the NGC guideline classification for this purpose. The axes of the proposed scheme have implications for designing formal models and structures for representing and authoring CPGs. This scheme also has implications for future research.

Classification↗

PubMed2000

GLIF3: the evolution of a guideline representation format.

The Guideline Interchange Format (GLIF) is a language for structured representation of guidelines. It was developed to facilitate sharing clinical guidelines. GLIF version 2 enabled modeling a guideline as a flowchart of structured steps, representing clinical actions and decisions. However, the attributes of structured constructs were defined as text strings that could not be parsed, and such guidelines could not be used for computer-based execution that requires automatic inference. GLIF3 is a new version of GLIF designed to support computer-based execution. GLIF3 builds upon the framework set by GLIF2 but augments it by introducing several new constructs and extending GLIF2 constructs to allow a more formal definition of decision criteria, action specifications and patient data. GLIF3 enables guideline encoding at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that can be incorporated into particular institutional information systems.

Decision Support Techniques↗