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Many microarray gene expression data sets have multiple ordered sample groups. Genes showing increasing/decreasing differential expression or differential gene-gene co-expression patterns can be biologically interesting. Statistically, we can conduct the analysis of ordered changes of population means and ordered changes of regression slopes. The well-developed isotonic regression can be considered...
For the identification of significant genes involved in specific diseases, microarray gene expression profiles have been widely used to prioritize candidate genes. In this paper, we propose a new gene ranking method that employs genegene relations extracted from literature along with gene expression scores obtained from microarrays. Here the genegene relations are extracted by taking a hybrid approach...
A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and it may provide much needed information for effective diagnosis, prognosis, and optimal therapy. However, despite considerable effort in studying cancer progressions, their molecular and genetic basis remains largely unknown. Microarray experiments can systematically assay gene...
In this paper, we present a novel method based on spectral bipartitioning, traditionally used for finding min-cuts in graphs, for classification of cancer using microarray data. Our method is applied to five publicly available datasets of acute leukemia, colon cancer, ovarian cancer, prostate cancer and diffuse large B-cell lymphoma, and is shown to have classification accuracy comparable to that...
Discovering biomarkers using mass spectrometry (MS) and microarray expression profiles is a promising strategy in molecular diagnosis. Here, the authors proposed a new pipeline for biomarker discovery that integrates disease information for proteins and genes, expression profiles in both genomic and proteomic levels, and protein-protein interactions (PPIs) to discover high confidence network biomarkers...
Microarray technology has enabled us to simultaneously measure the expression of thousands of genes. Using this high-throughput technology, we can examine subtle genetic changes between biological samples and build predictive models for clinical applications. Although microarrays have dramatically increased the rate of data collection, sample size is still a major issue when selecting features. Previous...
We describe the development of a very large-scale causal, computable model of biology and its specific application in the identification of molecular cause and effect hypotheses of mechanisms underlying the effects of androgen stimulation in the LNCaP prostate carcinoma cell line. In contrast to previous LNCaP studies in which genes have been hierarchically clustered by their pattern of response to...
We describe the development of a very large-scale causal, computable model of biology and its specific application in the identification of molecular cause and effect hypotheses of mechanisms underlying the effects of androgen stimulation in the LNCaP prostate carcinoma cell line. In contrast to previous LNCaP studies in which genes have been hierarchically clustered by their pattern of response to...
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