Income distribution, socioeconomic status, and self rated health in the United States: multilevel analysis

Objective: To determine the effect of inequalities in income within a state on self rated health status while controlling for individual characteristics such as socioeconomic status.

Analysis of gene expression during myc oncogene-induced lymphomagenesis in the bursa of Fabricius

The transcriptional effects of deregulated myc gene overexpression are implicated in tumorigenesis in a spectrum of experimental and naturally occurring neoplasms. In follicles of the chicken bursa of Fabricius, myc induction of B-cell neoplasia requires a target cell population present during early bursal development and progresses through preneoplastic transformed follicles to metastatic lymphomas. We developed a chicken immune system cDNA microarray to analyze broad changes in gene expression that occur during normal embryonic B-cell development and during myc-induced neoplastic transformation in the bursa. The number of mRNAs showing at least 3-fold change was greater during myc-induced lymphomagenesis than during normal development, and hierarchical cluster analysis of expression patterns revealed that levels of several hundred mRNAs varied in concert with levels of myc overexpression. A set of 41 mRNAs were most consistently elevated in myc-overexpressing preneoplastic and neoplastic cells, most involved in processes thought to be subject to regulation by Myc. The mRNAs for another cluster of genes were overexpressed in neoplasia independent of myc expression level, including a small subset with the expression signature of embryonic bursal lymphocytes. Overexpression of myc, and some of the genes overexpressed with myc, may be important for generation of preneoplastic transformed follicles. However, expression profiles of late metastatic tumors showed a large variation in concert with myc expression levels, and some showed minimal myc overexpression. Therefore, high-level myc overexpression may be more important in the early induction of these lymphomas than in maintenance of late-stage metastases.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.