Search

Background: Social-environmental data obtained from the US Census is an important resource for understanding health disparities, but rarely is the full dataset utilized for analysis. A barrier to incorporating the full data is a lack of solid recommendations for variable selection, with researchers often hand-selecting a few variables. Thus, we evaluated the ability of empirical machine learning approaches to identify social-environmental factors having a true association with a health outcome., Methods: We compared several popular machine learning methods, including penalized regressions (e.g. lasso, elastic net), and tree ensemble methods. Via simulation, we assessed the methods' ability to identify census variables truly associated with binary and continuous outcomes while minimizing false positive results (10 true associations, 1000 total variables). We applied the most promising method to the full census data (p = 14,663 variables) linked to prostate cancer registry data (n = 76,186 cases) to identify social-environmental factors associated with advanced prostate cancer., Results: In simulations, we found that elastic net identified many true-positive variables, while lasso provided good control of false positives. Using a combined measure of accuracy, hierarchical clustering based on Spearman's correlation with sparse group lasso regression performed the best overall. Bayesian Adaptive Regression Trees outperformed other tree ensemble methods, but not the sparse group lasso. In the full dataset, the sparse group lasso successfully identified a subset of variables, three of which replicated earlier findings., Conclusions: This analysis demonstrated the potential of empirical machine learning approaches to identify a small subset of census variables having a true association with the outcome, and that replicate across empiric methods. Sparse clustered regression models performed best, as they identified many true positive variables while controlling false positive discoveries.

Background: It is accepted that a woman's lifetime risk of developing breast cancer after menopause is reduced by early full term pregnancy and multiparity. This phenomenon is thought to be associated with the development and differentiation of the breast during pregnancy., Methods: In order to understand the underlying molecular mechanisms of pregnancy induced breast cancer protection, we profiled and compared the transcriptomes of normal breast tissue biopsies from 71 parous (P) and 42 nulliparous (NP) healthy postmenopausal women using Affymetrix Human Genome U133 Plus 2.0 arrays. To validate the results, we performed real time PCR and immunohistochemistry., Results: We identified 305 differentially expressed probesets (208 distinct genes). Of these, 267 probesets were up- and 38 down-regulated in parous breast samples; bioinformatics analysis using gene ontology enrichment revealed that up-regulated genes in the parous breast represented biological processes involving differentiation and development, anchoring of epithelial cells to the basement membrane, hemidesmosome and cell-substrate junction assembly, mRNA and RNA metabolic processes and RNA splicing machinery. The down-regulated genes represented biological processes that comprised cell proliferation, regulation of IGF-like growth factor receptor signaling, somatic stem cell maintenance, muscle cell differentiation and apoptosis., Conclusions: This study suggests that the differentiation of the breast imprints a genomic signature that is centered in the mRNA processing reactome. These findings indicate that pregnancy may induce a safeguard mechanism at post-transcriptional level that maintains the fidelity of the transcriptional process.