Transition dependency: a gene-gene interactionmeasure for times seriesmicroarray data
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas
Advances in Data Analysis and Classification
Finite mixtures of unimodal beta and gamma densities and the $$k$$-bumps algorithm
Computational Statistics
Expert Systems with Applications: An International Journal
| Hi-index | 3.84 |
Summary: We propose a beta-mixture model approach to solve a variety of problems related to correlations of gene-expression levels. For example, in meta-analyses of microarray gene-expression datasets, a threshold value of correlation coefficients for gene-expression levels is used to decide whether gene-expression levels are strongly correlated across studies. Ad hoc threshold values such as 0.5 are often used. In this paper, we use a beta-mixture model approach to divide the correlation coefficients into several populations so that the large correlation coefficients can be identified. Another important application of the proposed method is in finding co-expressed genes. Two examples are provided to illustrate both applications. Through our analysis, we also discover that the popular model selection criteria BIC and AIC are not suitable for the beta-mixture model. To determine the number of components in the mixture model, we suggest an alternative criterion, ICL--BIC, which is shown to perform better in selecting the correct mixture model. Contact: yuanji@mdanderson.org Supplementary information: http://odin.mdacc.tmc.edu/~yuanj/highcorgeneanno.html