In this study, a software tool (IFGFA) for identification of featured genes from gene expression data based on latent factor analysis was developed. Despite the availability of computational methods and statistical models appropriate for analyzing special genomic data, IFGFA provides a platform for predicting colon cancer-related genes and can be applied to other cancer types. The computational framework behind IFGFA is based on the well-established Bayesian factor and regression model and prior knowledge about the gene from OMIM.
The aim of this study was to explore the correlation between the expression levels of Gli1 and p53 in pancreatic ductal adenocarcinoma (PDAC) and its pathological significance. Immunohistochemistry (IHC) was employed to measure the expression level of Gli1 and p53 in 85 sets of paraffin-embedded PDAC and corresponding para-carcinoma tissue specimens. The relationship between these results and the respective patients’ clinicopathologic parameters was analyzed.
This study aimed to analyze the robustness of mixed models for the study of genotype-environment interactions (G x E). Simulated unbalancing of real data was used to determine if the method could predict missing genotypes and select stable genotypes. Data from multi-environment trials containing 55 maize hybrids, collected during the 2005-2006 harvest season, were used in this study.