Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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Breast cancer and osteoporosis are two most common diseases in postmenopausal women. Both diseases are multi-factorial and involve complex interactions of many genes. Since it is very difficult to review all published papers manually to understand interaction between genes pertaining to these two diseases, we employed text mining system which is an automated approach to search for these gene interactions. Two gene lists were first constructed. The first one contained genes that may be involved in breast cancer, and the second one included those that may be involved in osteoporosis. Potential transitive or indirect associations between two gene terms were determined using transitive closure on the direct associations extracted on the basis of co-occurrence of gene terms in the abstracts. The transitive associations were ranked using a graph-based weight scoring algorithm. With this scoring method, the top 10 gene pairs that are most likely associated with these two diseases were found to be p53/osteocalcin, VEGF/IGF-1, BRAC1/osteocalcin, p53/IL-6, IGFBP3/ESR-α, COMT/CYP1A1, p53/OPG, VEGF/OPG, and IGFBP3/RANK. This study also revealed a potential link of P53 in both diseases. Further investigations are required to characterize and confirm this association.