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Feature subset selection for outcome prediction is a critical issue in large scale microarray experiments in cancer research. This paper introduces an integrative approach that combines significant gene expression analysis, the genetic algorithm and machine learning for selecting informative gene markers and for predicting tumor outcomes including survival outcomes. In case of survival data, full...
Characterized by simultaneous measurement of the effects of experimental factors and their interactions, the economic and efficient factorial design is well accepted in microarray studies. To date, the only statistical method for analyzing microarray data obtained using factorial design has been the analysis of variance (ANOVA) model which is a gene by gene approach and relies on multiple assumptions...
Snow accumulation is a significant factor for determining sources and amounts of seasonal runoff for a variety of applications. Most current estimates of snow depth, however, consist of manual (spot) measurements combined with weather models, and don't capture snow depth information at appropriate scales for regional forecasting or local use. Remote sensing data have the potential to determine snow...
Visible and Near-Infrared (VNIR) Multispectral and hyperspectral data are compared for their potential to assist with disaster response and recovery via detailed mapping of the distribution of specific surface materials. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data at two spatial resolutions are used to assess the effects of spatial resolution on material mapping capabilities. AVIRIS...
The microarray-based gene expression time-course experiments represent an important research design in biomedical studies. Current methods for analyzing microarray time-course data ignore individual differences in treatment responses or time-course patterns and have difficulties in handling missing values. We introduce the latent growth curve models popular in use in longitudinal epidemiology studies...
Remote mapping of night lights has been used for decades for mapping urbanization and the global distribution of human activity. Most of this has been accomplished using remote sensing data from the Defense Meteorological Satellite Program (DMSP). The coarse spatial and spectral resolution of DMSP, however, has precluded discrimination of lighting types or spectral characteristics. Recent demonstrations...
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