Characterizing genes by marginal expression distribution

Edward Wijaya, Hajime Harada, Paul Horton

研究成果: Conference contribution


We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal distribution of the gene. The base distributions used were normal, lognormal and gamma. The expectation-maximization algorithm was used to learn the model parameters. Experiments with articifial data were performed to ascertain the robustness of learning. Applying the procedure to data from two publicly available microarray datasets, we conclude that lognormal performed the best function for modeling the marginal distributions of gene expression. Our results should provide guidances in the development of informed priors or gene specific normalization for use with gene network inference algorithms.

主出版物標題Advances in Computational Science and Engineering
主出版物子標題Second International Conference, FGCN 2008, Workshops and Symposia, Sanya, Hainan Island, China, December 13-15, 2008. Revised Selected Papers
編輯Tai-hoon Kim, Laurence T. Yang, Jong Hyuk Park, Alan Chin-Chen Chang, Thanos Vasilakos, Yan Zhang, Damien Sauveron, Xingang Wang, Young-Sik Jeong
出版狀態Published - 2009 十二月 1


名字Communications in Computer and Information Science

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)
  • 數學(全部)


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