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Monday, May 01, 2000

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Stanford Medical Informatics Preprint Archive Ontology-Oriented Design and Programming
"In the construction of both conventional software and intelligent systems, developers continue to seek higher level abstractions that both can aid in conceptual modeling and can assist in implementation and maintenance. In recent years, the artificial intelligence community has placed considerable attention on the notion of explicit ontologies -- shared conceptualizations of application areas that define the salient concepts and relationships among concepts. Such ontologies, when joined with well defined problem-solving methods, provide convenient formalisms for modeling and for implementing solutions to application tasks. This chapter reviews the motivation for seeking such high-level abstractions, and summarizes recent successes in building systems from reusable domain ontologies and problem-solving methods. As the environment for software execution moves from individual workstations to the Internet at large, casting new software applications in terms of these high-level abstractions may make complex systems both easier to build and easier to maintain. "
Gene Ontology Consortium
"This is the home of the Gene Ontology Consortium. The goal of the Gene Ontology consortium is to produce a dynamic controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing."

"The three organising principles of GO are molecular function, biological process and cellular component. A gene product has one or more molecular functions and is used in one or more biological processes; it may be, or may be associated with, one or more cellular components."

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Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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