Building Large Knowledge Bases in Molecular Biology

O. Schmeltzer, C. Médigue, P. Uvietta, F. Rechenmann, F. Dorkeld, G. Perrière, and C. Gautier

Large scale genome sequencing projects are now producing huge amounts of data which can be readily stored and managed within data base management systems, and analyzed using dedicated software packages. The results of these analyzes should also be stored with the input DNA sequences. The increasing complexity and size of the objects to be described and managed have led biologists to rely on advanced data models such as the object-oriented model. As a joint effort between our computer sdence and molecular biology research projects, the knowledge bases we have developed in molecular genetics have shown however that the basic object- oriented model is not fully adapted to the complexity of some biological situations encountered. Advanced descriptive capabilities, provided only by knowledge models originated from the AI field, are required. Composite or evolving objects, multiple viewpoints, constraints, tasks and methods, textual annotations are some examples of such capabilities. They are illustrated by biological situations for which they appeared to be necessary. Supporting powerful reasoning mechanisms (e.g. object classification, constraint propagation or qualitative simulators), they allow the development of large knowledge bases in molecular biology. These knowledge bases are expected to become the adequate support for co-operative distributed research efforts.


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