The identification of candidate genes and construction of gene networks both make use of simple text mining techniques. Concept profiles are constructed for each disease and for each gene. Each concept (a disease or a gene) is represented by an ordered list of words and terms most closely associated with the concept. The set of words and terms is complied from the contents of the approximately 80,000 PubMed abstracts [8] that have been manually associated with one or more human genes in the NCBI Entrez Gene database [9], using natural language processing [10]. Pairs of concepts, such as two genes or a disease and a gene, are linked by the overlap of their keyterm profiles. We call the resulting gene-gene network a KnowledgeNet, since it is derived directly from knowledge in the literature. Only two types of concept, gene and disease, are discussed in this paper. However, the KnowledgeNet can also be used in others ways, for example investigating the relationship between a biological process (e.g. glycolysis) and genes.
