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Table 1. Distribution of gene expression in purified hematopoietic stem and progenitor cells

in unknown title
by unknown authors 2002
"... In PAGE 10: ... We first analyzed how many genes were expressed in each population by picking up genes with expression levels above the cutoff line defined by a compensation method recommended by the ma nufacture (see Methods) . As shown in Table1 , ~42 % of genes on the chips were detectable in each population. Among these, around 23% of genes were expressed at low levels in each population of cells.... In PAGE 11: ... 0.930, respectively. Thus, the numerical correlation values correctly reflect the hierarchical relationship among these purified populations in physiological hematopoiesis (Figure 1). Out of ~2,000 known genes which passed the cutoff line (see Methods), two groups of genes were classified as either hematopoiesis - or non - hematopoiesis - affiliated genes according to their tissue - specific expression or functions ( Table1 ). These genes are shown in Figures 2 and 4.... In PAGE 11: ... These genes are shown in Figures 2 and 4. Genes related to multiple non - hematopoietic tissues are predominantly expressed in hematopoietic stem cells Transcripts of a variety of non - hematopoietic genes were detected in early hematopoiesis ( Table1 ). HSCs expressed 43 out of 58 genes specific to non - hematopoietic tissues detected by chip hy bridization.... In PAGE 13: ... The final partition of the 5,223 genes/ESTs resulted i n 100 clusters, each containing a different number of genes (see Methods ). We focused on genes that were dominantly expressed in each population, grouping them into 4 categories (Figure 4 and Table1 ). For personal use only.... In PAGE 14: ...The clustering analysis revealed again that the ma jority of non - hematopoiesis - affiliated genes fell into Category A ( Table1 ). Category A also contained genes that might play a role in the regulation of stem cell properties such as self - renewal (Figure 4A).... In PAGE 14: ... This data is compatible with the fact that MPPs are highly proliferative cells (Figure 1B) and suggests that MPPs are at a priming stage for both myeloid and lymphoid differentiation. The majority of genes preferentially ex pressed in CLPs (41% of hematopoietic related, Category C) and CMPs (25% of hematopoietic related, Category D) were lymphoid and myeloid genes, respectively ( Table1 and Figure 4). Genes in Category C included B, T, and NK lymphoid associated genes (i.... In PAGE 22: ... Lineage promiscuity is distributed in a hierarchical and asymmetric fashion. Table1 . Results of an Affymetrix microarray analysis targeting purified hematopoietic stem and progenitor cells.... ..."

Table 2 Expression of androgen regulated genes in pubertal prostate

in unknown title
by unknown authors
"... In PAGE 7: ... The statistically significant genes in the SAM analysis were compared with the published data of androgen responsive genes (ARGs). Several of these were found to be common ( Table2 ). Genes such as TMEPAI, NKx3.... In PAGE 20: ...ubertal genes (underexpressed in pubertal vs. adult prostate with mean-centered average less than -0.45) and the top 300 LNCaP androgen genes (overexpressed in R1881-treated cells with mean-centered average gt;2). Genes highlighted in bold appear in Table2 , and those italicized appear in Fig. 5.... ..."

Table 1: Gene Expression Datasets

in Mining top-k covering rule groups for gene expression data
by Gao Cong 2005
"... In PAGE 8: ... The entropy discretization algorithm also per- forms feature selection as part of its process. Table1 shows the characteristics of the four discretized datasets: the number of orig- inal genes, the number of genes after discretization, the two class labels (class 1 and class 0), and the number of rows for training and test data. All experiments presented here use the class 1 as the consequent; we have found that using the other consequent consis- tently yields qualitatively similar results.... ..."
Cited by 11

Table 3: List of the top 15 most significantly expressed genes in MCF-7 cells or in skeletal muscle as determined using the Actichip microarray

in unknown title
by unknown authors 2007
"... In PAGE 6: ...85; FDR = 0 %). This list was highly enriched in marker genes char- acteristic for either epithelial or skeletal muscle cells ( Table3 ), in good agreement with the expression patterns expected from an a priori reasoning based on biological knowledge. Importantly, we obtained similar results through SAM analysis using three randomly chosen exper- iments over the entire series of assays (data not shown) revealing that a limited number of repeats would be suffi- cient to obtain reliable data with Actichip.... In PAGE 9: ... In this study, we analysed two well-contrasted RNA sam- ples, each characterised by a specific organisation of their actin cytoskeleton and by known marker genes. Many of these genes were found significantly expressed using Acti- chip ( Table3 ), underlining the reliability of this array as a transcriptome analysis platform and its value for the char- acterisation and classification of biological samples based on their transcriptome profiles. Our data further showed that Actichip not only detects reliably qualitative gene expression changes, but has also the potential to accu- rately measure the amplitude of these variations (Figure 3).... ..."

Table 2. Representative results from partial time-shift analysis. Bin Shift Range shows the wildtype expression I nduction range (in hours) for the genes upregu- lated at 60 hrs AED in the mutants. Shift Bin Size: Number of genes in this shift. Func Bin Size: Number of genes with this GO-ID in this shift. Genes with this GO-ID: Total no of genes with this GO-ID among the 242 genes up-regulated at 60 hrs AED that have a GO-ID and are also present in the wildtype data set.

in Time-window analysis of developmental gene expression data with multiple genetic backgrounds
by Tamir Tuller, Efrat Oron, Erez Makavy, Daniel A. Chamovitz, Benny Chor 2005
"... In PAGE 9: ... Our results for putative time- shifted genes compared to only one time point 60 in the mutants (problem 2, where the conditions of the mutants include only time point 60 ) are summarized in table 2. Table2 shows that both types of behaviors were identi ed. The rst two rows of Table 2 show genes that are up regulated in two CSN mutants at 60 hours, while in wildtype these genes peak during early or late embryogenesis (0 24 hrs AED).... In PAGE 9: ... Table 2 shows that both types of behaviors were identi ed. The rst two rows of Table2 show genes that are up regulated in two CSN mutants at 60 hours, while in wildtype these genes peak during early or late embryogenesis (0 24 hrs AED). At the other extreme, the last two rows in Table 2 show sets of genes that are induced in a CSN mutant at 60 hours, while in the wildtype, these genes normally peak either during metamorphosis (149 161 hrs AED) or in old adults (527 827 hours from hatching).... In PAGE 9: ...n table 2. Table 2 shows that both types of behaviors were identi ed. The rst two rows of Table 2 show genes that are up regulated in two CSN mutants at 60 hours, while in wildtype these genes peak during early or late embryogenesis (0 24 hrs AED). At the other extreme, the last two rows in Table2 show sets of genes that are induced in a CSN mutant at 60 hours, while in the wildtype, these genes normally peak either during metamorphosis (149 161 hrs AED) or in old adults (527 827 hours from hatching). 4.... ..."
Cited by 1

Table 1. The Characteristics of Gene Expression Data Sets

in Mining Quantitative Maximal Hyperclique Patterns: A Summary of Results
by Yaochun Huang, Hui Xiong, Weili Wu, Sam Y. Sung
"... In PAGE 3: ... 4 Experimental Evaluation Experimental Setup Our experiments were performed on two real-life gene expression data sets, Colon Cancer and NCI [2, 7]. Table1 shows some charac-... ..."

Table 1. The Characteristics of Gene Expression Data Sets

in Mining Quantitative Maximal Hyperclique Patterns: A Summary of Results
by Yaochun Huang, Hui Xiong, Weili Wu, Sam Y. Sung
"... In PAGE 3: ... 4 Experimental Evaluation Experimental Setup Our experiments were performed on two real-life gene expression data sets, Colon Cancer and NCI [2, 7]. Table1 shows some charac-... ..."

TABLE I GENE EXPRESSION DATA MATRIX

in Biclustering Algorithms for Biological Data Analysis: A Survey
by Sara C. Madeira, Arlindo L. Oliveira

Table 5.1 Sample data for genes and gene expressions

in ABSTRACT ZECHMAN, EMILY MICHELLE. Improving Predictability of Simulation Models using Evolutionary Computation-Based Methods for Model Error Correction. (Under the
by Direction Of S. Ranji Ranjithan

TABLE I DATA SETS USED FOR CLASSIFICATION ANALYSIS.

in Principal direction linear oracle for gene expression ensemble classification
by Leif E. Peterson, Matthew A. Coleman 2007
Cited by 2
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