An automated system to decode microarray platforms from NCBI Gene Expression Omnibus (GEO). We also built a web server to enable users to re-annotate their microarray results and find existing experiments with cross-platform comparison. The system relates probe IDs to Entrez Gene identifiers through a universal gene identifier table for all species.
We are developing methods to describe the semantic content in images using ontologies, explicit representations of the entities and relations in biomedicine.
A new system to address the heightened concerns about bioterrorism and the need to alter the traditional biosurveillance model to properly manage these concerns. BioSTORM rapidly integrates and processes multiple diverse data feeds using a variety of problem solving and analysis techniques to give timely analysis of multiple, non-specific, pre-diagnostic indicators often drawn from many data sources.
Though detection and analysis of temporal trends and patterns in data is central to informatics research, the biomedical field has no standard means for making them. Chronus is a database mediator that supports temporal queries on relational databases by extending the SQL query language and standard relational model. It allows users to easily write complex temporal queries using an expressive SQL-based temporal query language.
Funded by the National Library of Medicine, the Butte Lab is integrating large-scale data sets across measurement modalities to create new knowledge and testable hypotheses in the areas of diabetes and obesity.
We are developing the DICOM Ontology (DO), an ontology that will be a single common reference information model for the imaging domain.
EON is an extensible architecture for developing decision-support tools for various aspects of protocol-based care. Initially developed to create systems for executing clinical trial protocols for the treatment of cancer and HIV infection, it has been extended for the management of chronic diseases and other types of guidelines and is currently in use in the Athena system.
We are building an ontology-based architecture to support the management of clinical trials. At the core of our architecture is a suite of ontologies that we call Epoch that encodes knowledge about the clinical trial domain that is relevant to trial management applications.
The Butte Lab has been funded by the National Institute for General Medical Sciences to build a novel gene-expression-based classification scheme for diseases across the entire field of medicine.
Genotext is a text-mining tool to relate the free-text annotations of genome-scale experiments to the Unified Medical Language System (UMLS), published by the Butte Lab in Nature Biotechnology (January 2006).
Images are pervasive in biomedicine. But unlike other types of biomedical data, it is challenging to index and search the contents of images. In the IQ project, we are developing semantic methods for composing and executing searches for the contents of images.
The National Center for Biomedical Ontology is a consortium of leading biologists, clinicians, informaticians, and ontologists who develop innovative technology and methods that allow scientists to create, disseminate, and manage biomedical information and knowledge in machine-processable form. Our vision is that all biomedical knowledge and data are disseminated on the Internet using principled ontologies, such that the knowledge and data are semantically interoperable and useful for furthering biomedical science and clinical care.
Protégé is a free, open-source platform that provides its community of more than 80,000 users with a suite of tools to construct domain models and knowledge-based applications with ontologies. At its core, Protégé implements a rich set of knowledge-modeling structures and actions that support the creation, visualization, and manipulation of ontologies in various representation formats. Protégé can be customized to provide domain-friendly support for creating knowledge models and entering data. Further, Protégé can be extended by way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools and applications.
Initially described in the Proceedings of the National Academy of Science (October 2000), Relevance Networks allow one to build correlational networks of features, whether they represent genes, phenotypic or clinical measurements.
Funded by the California Institute for Regenerative Medicine, the National Cancer Institute, and the Stanford Cancer Center, the Butte Lab is working in close collaboration with Ken Weinberg to determine molecular profiles of stem cell development.
