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PubMed2002

An integrated database of chemosensitivity to 55 anticancer drugs and gene expression profiles of 39 human cancer cell lines.

To explore genes that determine the sensitivity of cancer cells to anticancer drugs, we investigated using cDNA microarrays the expression of 9216 genes in 39 human cancer cell lines pharmacologically characterized on treatment with various anticancer drugs. A bioinformatical approach was then exploited to identify genes related to anticancer drug sensitivity. An integrated database of gene expression and drug sensitivity profiles was constructed and used to identify genes with expression patterns that showed significant correlation to patterns of drug responsiveness. As a result, sets of genes were extracted for each of the 55 anticancer drugs examined. Whereas some genes commonly correlated with various classes of anticancer drugs, other genes correlated only with specific drugs with similar mechanisms of action. This latter group of genes may reflect the efficacy of each class of drugs. Therefore, the integrated database approach of gene expression and chemosensitivity profiles may be useful in the development of systems to predict drug efficacies of cancer cells by examining the expression levels of particular genes.

Antineoplastic Agents↗
PubMed2002

Automating data acquisition into ontologies from pharmacogenetics relational data sources using declarative object definitions and XML.

Ontologies are useful for organizing large numbers of concepts having complex relationships, such as the breadth of genetic and clinical knowledge in pharmacogenomics. But because ontologies change and knowledge evolves, it is time consuming to maintain stable mappings to external data sources that are in relational format. We propose a method for interfacing ontology models with data acquisition from external relational data sources. This method uses a declarative interface between the ontology and the data source, and this interface is modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML, and data integrity is checked by validating the XML submission with an XML schema. We have implemented this approach in PharmGKB (http://www.pharmgkb.org/), a pharmacogenetics knowledge base. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence information. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis.

Databases, Factual↗
PubMed2002

The micro-macro spectrum of medical informatics challenges: from molecular medicine to transforming health care in a globalizing society.

BACKGROUND: Medical informatics has always encompassed a very broad spectrum of techniques for clinical and biomedical research, education and practice. There has been a concomitant variety of depth of specialization, ranging from the routine application of information processing methods to cutting-edge research on fundamental problems of computer-based systems and their relations to cognition and perception in biomedicine. OBJECTIVES: Challenges for the field can be placed in perspective by considering the scale of each--from the highly detailed scientific problems in bioinformatics and emerging molecular medicine to the broad and complex social problems of introducing medical informatics into web-related global settings. METHODS: The scale of an informatics problem is not only determined by the inherent physical space in which it exists, but also by the conceptual complexity that it involves, reinforcing the need to investigate the semantic web within which medical informatics is defined. RESULTS AND CONCLUSION: Bioinformatics, biomedical imaging and language understanding provide examples that anchor research and practice in biomedical informatics at the detailed, scientific end of the spectrum. Traditional concerns of medical informatics in the clinical arena make up the broad mid-range of the spectrum, while novel social interaction models of competition and cooperation will be needed to understand the implications of distributed health information technology for individual and societal change in an increasingly interconnected world.

Databases, Factual↗
PubMed1993

State v. Olivas.

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Blood Specimen Collection↗