The United States of America, As Represented by the Secretary, Department of Health and Human Services

The Texas A&m University System

Agilent Technologies, Inc.

Yidong Chen

Edward R Dougherty

Michael L Bittner

Zohar H Yakhini

Paul S Meltzer

Amir Ben-Dor

Yuan Jiang

Nick Sampas

Yoganand Balagurunathan

Seungchan Kim

Jeff Trent

Ashani Weeraratna

Simulating a microarray includes defining a number of parameters. A microarray is generated according to the parameters using an imaging procedure. The microarray is compared to a known value, and the imaging procedure is evaluated in response to the comparison. A simulated microarray image can be generated based on parameters. The simulated microarray can be associated with known values. An imaging procedure is applied to the simulated microarray image to generate observed values. The known values (e.g., intensities) can be compared to the observed values to evaluate the imaging procedure.

Simulating microarrays using a parameterized model

The overexpression of certain marker genes including Wnt5a has been found useful in the identification of more aggressive forms of malignant melanoma. Therefore, the overexpression of these genes in tumor samples of malignant melanoma may be useful in the diagnosis, profiling, and treatment of patients suffering from this disease. Inhibitors of Wnt5a activity may be useful in the treatment of aggressive forms of malignant melanoma. Inhibition of Wnt5a activity may be effected by any method including anti-sense therapy, gene therapy, and pharmaceutical intervention.

Classifying cancers

Relatedness between genes is quantified by constructing nonlinear models predicting gene expression. Effectiveness of the model is evaluated to provide a measurement of the relatedness of genes associated with the model. Various types of models, including full-logic or neural networks can be constructed. A graphical user interface presents results of the analysis to allow evaluation by a user. Each gene's contribution to the measurement of relatedness can be shown on a graph, and graphical representations of models used to predict gene expression can be displayed.

Quantifying gene relatedness via nonlinear prediction of gene

Gene expression can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA micro-array. Comparison of gene expression levels arising from co-hybridized samples is achieved by taking ratios of average expression levels for individual genes. In an image-processing phase, a method of image segmentation identifies cDNA target sites in a cDNA micro-array image. The resulting cDNA target sites are analyzed based on a hypothesis test and confidence interval to quantify the significance of observed differences in expression ratios. In particular, the probability density of the ratio and the maximum-likelihood estimator for the distribution are derived, and an iterative procedure for signal calibration is developed.

Ratio-based decisions and the quantitative analysis of cDNA micro-array images

Growing community of inventors

Rockville, MD, United States of America

Yidong Chen