An application of bioinformatics and text mining to the discovery of novel genes related to bone biology.

The treatment and management of complex genetic diseases such as osteoporosis can greatly benefit from the integration of relevant research across many different disciplines. We created a text mining tool that analyzes the PubMed literature database and integrates the available genomic information to provide a detailed mapping of the genes and their interrelationships within a particular network such as osteoporosis. The results obtained from our text mining program show that existing genomic data within the PubMed database can effectively be used to predict potentially novel target genes for osteoporosis research that have not previously been reported in the literature. To filter the most significant findings, we developed a ranking system to rate our predicted novel genes. Some of our predicted genes ranked higher than those currently studied, suggesting that they may be of particular interest from a therapeutic standpoint. A preliminary analysis of the current biomedical literature in our research area using our tool suggests that S100A12, as well as a group of SMAD genes previously unstudied in relation to osteoporosis, may be highly relevant to the mechanism of action of bisphosphonates, that the function of osteocytes may be influenced by a family of important interleukins and interleukin-related molecules, and that the FYN oncogene may play an important role in regulating the apoptosis of bone cells in the context of degenerative bone diseases. An evaluation of our tool's predictive ability with an analysis of PubMed literature published before the year 2000 in the area of osteoporosis research shows that many of its top-rated novel target genes from that analysis were later studied and shown to be relevant to osteoporosis in the period between 2000 and 2006. We believe that our tool will be beneficial to researchers in the field of orthopaedics seeking to identify novel target genes in their research area, and it will allow them to delve deeper into the complex interplay between genes, biological systems and diseases.