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Author manuscript, published in IEEE MCD'2005 international workshop on Mining Complex Data, Houston : United States (2005) Improving the Knowledge Discovery Process Using Ontologies Laurent Frisson,

The approach was developed in the context of a set of open challenges for the scientific community as they are introduced in the 2005 annual meeting of the European Society of Human Genetics (EIGHT). The approach is also used to solve problems of a more specialized nature, such as the classification and phylogenetic analysis of human DNA (GDP), in which it differs from more typical computer-aided methods that seek to perform an exhaustive search across all genes. The approach is based on a combination of new concepts in the field of ontology and new ontologies from the human genome project. This approach allows us to improve on existing techniques, improving the extraction and discovery of data and increasing the efficiency and accuracy of the data generated. Further, the approach allows it to be easily applied to other types of data than those typically used by computer-aided methods. Moreover, the approach significantly simplifies the implementation and allows for easy development of custom code to adapt these approaches for other applications. Keywords: ontology, database, ontology discovery, human genetics, bioinformatics, computer science, genetic algorithms, molecular biology, genetic medicine, genomics Abstract The goal of medical research is to identify genes and genetic diseases by analyzing a large body of patient gene expression data and their expression profiles in tissues. There is an increasing interest in the use of large databases for analyzing the data. However, the large databases also have their drawbacks, most notably their complexity. The use of large databases also requires the use of new, complex algorithms, which complicate the data mining process and increase the costs of a database. To overcome these obstacles, the human genome project (HIP) uses a system that uses ontologies to organize the information from the gene expression data. For many projects, the use of a large database to extract information is not a realistic option due to its complexity. To overcome this issue, it is important, to develop a knowledge discovery process using Ontologies, a set of rules for organizing scientific knowledge in a natural and human language. The goal of this paper is to present a new method for the discovery of knowledge in large databases. The method has a very simple interface, using generic ontologies, and it makes it easy to adapt it to other types of applications. The approach was developed in the context of a set of open challenges for the scientific community, i.e.

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