Biomedical subjects

Gary Stormo

Publications and source records attributed to Gary Stormo.

3 recordsLinked to original sources

PubMed2006

Training of the next generation of biostatisticians: a call to action in the U.S.

Two workshops (2001, 2003) were held by the National Institutes of Health (NIH) to examine the need to train more biostatisticians in the U.S. to meet the increasing opportunities in the biomedical research enterprise. The supply of new PhD graduates in biostatistics in the U.S. has been relatively steady for the past two decades while the demand has increased dramatically. These workshops concluded that a renewed effort must be made in the U.S., led in part by the NIH, to add to and expand the existing training programs to increase the supply. This article summarizes those two workshops and their recommendations. Some progress has been made through a new biostatistics training program with emphasis in bioinformatics sponsored by the National Institute of General Medical Sciences (NIGMS).

Biological Science Disciplines↗

PubMed2005

Application of genome-wide expression analysis to human health and disease.

The application of genome-wide expression analysis to a large-scale, multicentered program in critically ill patients poses a number of theoretical and technical challenges. We describe here an analytical and organizational approach to a systematic evaluation of the variance associated with genome-wide expression analysis specifically tailored to study human disease. We analyzed sources of variance in genome-wide expression analyses performed with commercial oligonucleotide arrays. In addition, variance in gene expression in human blood leukocytes caused by repeated sampling in the same subject, among different healthy subjects, among different leukocyte subpopulations, and the effect of traumatic injury, were also explored. We report that analytical variance caused by sample processing was acceptably small. Blood leukocyte gene expression in the same individual over a 24-h period was remarkably constant. In contrast, genome-wide expression varied significantly among different subjects and leukocyte subpopulations. Expectedly, traumatic injury induced dramatic changes in apparent gene expression that were greater in magnitude than the analytical noise and interindividual variance. We demonstrate that the development of a nation-wide program for gene expression analysis with careful attention to analytical details can reduce the variance in the clinical setting to a level where patterns of gene expression are informative among different healthy human subjects, and can be studied with confidence in human disease.

Cluster Analysis↗

PubMed2002

Role of gene expression microarray analysis in finding complex disease genes.

The promise of gene expression studies using microarray technology has inspired much new hope for finding complex diseases genes. It has become clear that complex diseases result from collective actions of many genetic and nongenetic factors. Therefore, genetic dissection of complex diseases should be carried out in a global context. The technology of gene expression microarray analysis (GEMA) can provide such global information on transcription activities of essentially all genes simultaneously. It is hoped that this promising technology can be applied to samples drawn from large-scale, well-defined genetic epidemiological studies and help us untangle the web of pathways leading to complex diseases. However, extremely noisy GEMA data pose serious challenges in terms of the statistical methodologies needed. Extensive work is needed in order to respond to the challenges before one can fully utilize the potential power provided by GEMA. We begin in this paper by identifying several statistical problems related to the application of GEMA to genetic epidemiological analysis, and consider study designs that might benefit from this promising new technology. While it is still too early to tell how much of the enormous potential of GEMA will be realized ultimately, its success will probably depend most critically on the ability of statistical genetics to rise to the challenge of mining information from a sea of noise.

Gene Expression↗