Abstract Shanfeng Zhu^*1, Yasushi Okuno^*2, Gozoh Tsujimoto² and Hiroshi Mamitsuka^{1, 2}

¹Bioinformatics Center, Institute for Chemical Research, Kyoto University ²Graduate School of Pharmaceutical Sciences, Kyoto University

Abstract: An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As bio-medical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html

Discussion
No comments yet...Be the first to comment.

 CONNOTEA  CITEULIKE FACEBOOK LINKED IN