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The RNA-binding protein TIAR (related to TIA-1 [T-cell-restricted intracellular antigen 1]) was shown to associate with subsets of mRNAs bearing U-rich sequences in their 3' untranslated regions. TIAR can function as a translational repressor, particularly in response to cytotoxic agents. Using unstressed colon cancer cells, collections of mRNAs associated(More)
The objective of this research is to model the mammographic parenchymal, ductal patterns and enhance the microcalcifications using deterministic fractal approach. According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two-dimensional (2-D) affine transformations. The(More)
A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in(More)
BACKGROUND In order to compare the gene expression profiles of human embryonic stem cell (hESC) lines and their differentiated progeny and to monitor feeder contaminations, we have examined gene expression in seven hESC lines and human fibroblast feeder cells using Illumina bead arrays that contain probes for 24,131 transcript probes. RESULTS A total of(More)
The discovery of regulatory networks is an important aspect in the post genomic research. The process requires integrated efforts of experimental and computational strategies by employing the systems biology approach. This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other data(More)
—The quantitative mapping of a database that represents a finite set of classified and/or unclassified data points may be decomposed into three distinctive learning tasks: 1) detection of the structure of each class model with locally mixture clusters; 2) estimation of the data distributions for each induced cluster inside each class; 3) classification of(More)
MOTIVATION A major challenge in post-genomic research has been to understand how physiological and pathological phenotypes arise from the networks of expressed genes. Here, we addressed this issue by developing an algorithm to mimic the behavior of regulatory networks in silico and to identify the dynamic response to disease and changing cellular(More)
An understanding of the regulatory mechanisms responsible for pluripotency in embryonic stem cells (ESCs) is critical for realizing their potential in medicine and science. Significant similarities exist among ESCs harvested from different species, yet major differences have also been observed. Here, by cross-species analysis of a large set of functional(More)
This paper presents a statistical model supported approach for enhanced segmentation and extraction of suspicious mass areas from mammographic images. With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-assisted(More)