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Atherosclerosis is a multifactorial disease involving a lot of genes and proteins recruited throughout its manifestation. The present study represents an integrative effort, coupling the results of a bioinformatic analysis based on microarray data of atherosclerotic aortic lesions of apoE knockout mice, a model widely used in atherosclerosis research, together with gene expression measurements of...
Propose: Idiopathic orbital inflammatory pseudotumors (IOIP) are inflammatory pseudotumors (IPT) of unknown etiology that develop in the orbit. Due to the lack of well-defined pathogenic mechanisms and diagnostic markers, diagnosis and treatment of this disease remain a significant challenge. Therefore, the aims of this study are to define the etiological factors of IOIP and to identify potential...
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis....
We described a combined multiple clustering approach to automatically identify chronic lymphocytic leukemia neoplastic population by flow cytometry immunophenotyping. Flow cytometry data from various specimens were preprocessed by data cross-linking and subset selection before undergoing subspace and consensus clustering. This approach was implemented as a Server-side application, with results comparable...
Glioblastomas are very aggressive cerebral tumors which are characterized by strong proliferative activity which is often assessed using the Ki-67 staining method. We propose a novel clustering method that exhibits high performance to detect Ki-67 hot-spots on immunohistochemical slides of glioblastomas.
Immunohistochemical color image segmentation has important application value for quantitative assessment of immunohistochemical image. In this paper, an automatic segmentation method was proposed according to characteristics of color immunohistochemical images. First of all, we established a Chromatics criteria in RGB space so that positive cells regional and negative cells region were separated automatically...
In this paper, we investigated the use of gene coexpression network analyses to identify potential biomarkers for breast carcinoma prognosis. The network mining algorithm CODENSE is used to identify highly connected genome-wide gene co-expression networks among a variety of cancer types, and the resulted gene clusters are applied to a series of breast cancer microarray sets to categorize the patients...
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