Information management systems for pharmacogenomics

The value of high-throughput genomic research is dramatically enhanced by association with key patient data. These data are generally available but of disparate quality and not typically directly associated. A system that could bring these disparate data sources into a common resource connected with...

Full description

Saved in:
Bibliographic Details
Published inPharmacogenomics Vol. 3; no. 5; pp. 651 - 667
Main Authors Thallinger, Gerhard G, Trajanoski, Slave, Stocker, Gernot, Trajanoski, Zlatko
Format Journal Article
LanguageEnglish
Published England Ashley Publications Ltd 01.09.2002
Future Medicine Ltd
Subjects
Algorithms
Animals
application
Computer Communication Networks - trends
Data mining
data warehouse
Humans
information management
Information Management - methods
Information Management - trends
Knowledge management
Medical research
Medicine, Experimental
network technologies
parallel algorithms
Pharmacogenetics
Pharmacogenetics - methods
Pharmacogenetics - trends
services
system
Online AccessGet full text

Cover

More Information
Summary:The value of high-throughput genomic research is dramatically enhanced by association with key patient data. These data are generally available but of disparate quality and not typically directly associated. A system that could bring these disparate data sources into a common resource connected with functional genomic data would be tremendously advantageous. However, the integration of clinical and accurate interpretation of the generated functional genomic data requires the development of information management systems capable of effectively capturing the data as well as tools to make that data accessible to the laboratory scientist or to the clinician. In this review these challenges and current information technology solutions associated with the management, storage and analysis of high-throughput data are highlighted. It is suggested that the development of a pharmacogenomic data management system which integrates public and proprietary databases, clinical datasets, and data mining tools embedded in a high-performance computing environment should include the following components: parallel processing systems, storage technologies, network technologies, databases and database management systems (DBMS), and application services.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ObjectType-Feature-3
ObjectType-Review-1
ISSN:1462-2416
1744-8042
DOI:10.1517/14622416.3.5.651