Publications by Year: 2004

BACKGROUND: Hormone therapy (HT) provides the most effective relief of menopausal symptoms. This therapy is associated with a decreased risk of osteoporosis and colorectal cancer but increased risks of cardiovascular disease (CVD), venous thrombosis, and breast cancer. Our objective was to identify which women should benefit from short-term HT by exploring the trade-off between symptom relief and risks of inducing disease.

METHODS: A Markov model simulates the effect of short-term (2 years) estrogen and progestin HT on life expectancy and quality-adjusted life expectancy (QALE) among 50-year-old menopausal women with intact uteri, using findings from the Women's Health Initiative. Quality-of-life (QOL) utility scores were derived from the literature. We assumed HT-affected QOL only during perimenopause, when it reduced symptoms by 80%.

RESULTS: Among asymptomatic women, short-term HT was associated with net losses in life expectancy and QALE of 1 to 3 months, depending on CVD risk. Women with mild or severe menopausal symptoms gained 3 to 4 months or 7 to 8 months of QALE, respectively. Among women at low risk for CVD, HT extended QALE if menopausal symptoms lowered QOL by as little as 4%. Among women at elevated CVD risk, HT extended QALE only if symptoms lowered QOL by at least 12%.

CONCLUSIONS: Hormone therapy is associated with losses in survival but gains in QALE for women with menopausal symptoms. Women expected to benefit from short-term HT can be identified by the severity of their menopausal symptoms and CVD risk.

Analysis of gene expression data obtained from microarrays presents a new set of challenges to machine learning modeling. In this domain, in which the number of variables far exceeds the number of cases, identifying relevant genes or groups of genes that are good markers for a particular classification is as important as achieving good classification performance. Although several machine learning algorithms have been proposed to address the latter, identification of gene markers has not been systematically pursued. In this article, we investigate several algorithms for selecting gene markers for classification. We test these algorithms using logistic regression, as this is a simple and efficient supervised learning algorithm. We demonstrate, using 10 different data sets, that a conditionally univariate algorithm constitutes a viable choice if a researcher is interested in quickly determining a set of gene expression levels that can serve as markers for disease. We show that the classification performance of logistic regression is not very different from that of more sophisticated algorithms that have been applied in previous studies, and that the gene selection in the logistic regression algorithm is reasonable in both cases. Furthermore, the algorithm is simple, its theoretical basis is well established, and our user-friendly implementation is now freely available on the internet, serving as a benchmarking tool for the development of new algorithms.

The vertebrate retina is comprised of seven major cell types that are generated in overlapping but well-defined intervals. To identify genes that might regulate retinal development, gene expression in the developing retina was profiled at multiple time points using serial analysis of gene expression (SAGE). The expression patterns of 1,051 genes that showed developmentally dynamic expression by SAGE were investigated using in situ hybridization. A molecular atlas of gene expression in the developing and mature retina was thereby constructed, along with a taxonomic classification of developmental gene expression patterns. Genes were identified that label both temporal and spatial subsets of mitotic progenitor cells. For each developing and mature major retinal cell type, genes selectively expressed in that cell type were identified. The gene expression profiles of retinal Müller glia and mitotic progenitor cells were found to be highly similar, suggesting that Müller glia might serve to produce multiple retinal cell types under the right conditions. In addition, multiple transcripts that were evolutionarily conserved that did not appear to encode open reading frames of more than 100 amino acids in length ("noncoding RNAs") were found to be dynamically and specifically expressed in developing and mature retinal cell types. Finally, many photoreceptor-enriched genes that mapped to chromosomal intervals containing retinal disease genes were identified. These data serve as a starting point for functional investigations of the roles of these genes in retinal development and physiology.