scholarly journals Correlation of Dynamic PET and Gene Array Data in Patients with Gastrointestinal Stromal Tumors

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Ludwig G. Strauss ◽  
Antonia Dimitrakopoulou-Strauss ◽  
Dirk Koczan ◽  
Leyun Pan ◽  
Peter Hohenberger
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gastrointestinal Stromal Tumors ◽  
Gene Array ◽  
Tracer Uptake ◽  
Cell Cycle Gene ◽  
Dynamic Pet ◽  
Expression Data ◽  
Array Data ◽  
Stromal Tumors

Introduction. The results obtained with dynamic PET (dPET) were compared to gene expression data obtained in patients with gastrointestinal stromal tumors (GIST). The primary aim was to assess the association of the dPET results and gene expression data.Material and Methods. dPET was performed following the injection of F-18-fluorodeoxyglucose (FDG) in 22 patients with GIST. All patients were examined prior to surgery for staging purpose. Compartment and noncompartment models were used for the quantitative evaluation of the dPET examinations. Gene array data were based on tumor specimen obtained by surgery after the PET examinations.Results. The data analysis revealed significant correlations for the dPET parameters and the expression of zinc finger genes (znf43, znf85, znf91, znf189). Furthermore, the transport of FDG (k1) was associated with VEGF-A. The cell cycle gene cyclin-dependent kinase inhibitor 1C was correlated with the maximum tracer uptake (SUVmax) in the tumors.Conclusions. The data demonstrate a dependency of the tracer kinetics on genes associated with prognosis in GIST. Furthermore, angiogenesis and cell proliferation have an impact on the tracer uptake.

scholarly journals Microarray missing values imputation methods: Critical analysis review

2009 ◽  
Vol 6 (2) ◽  
pp. 165-190 ◽  
Author(s):  
Mou'ath Hourani ◽  
Emary El
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Missing Values ◽  
Gene Array ◽  
Estimation Method ◽  
Least Square ◽  
Support Vector ◽  
Expression Data ◽  
Imputation Methods ◽  
Value Estimation

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.

scholarly journals Unbiased Boolean analysis of public gene expression data for cell cycle gene identification

2019 ◽  
Vol 30 (14) ◽  
pp. 1770-1779 ◽  
Author(s):  
Sarah A. Dabydeen ◽  
Arshad Desai ◽  
Debashis Sahoo
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Gene Expression Data ◽  
Boolean Logic ◽  
Cell Cycle Gene ◽  
Data Sets ◽  
Expression Data ◽  
Cell Cycle Genes ◽  
Logic Approach ◽  
Definition Of

Cell proliferation is essential for the development and maintenance of all organisms and is dysregulated in cancer. Using synchronized cells progressing through the cell cycle, pioneering microarray studies defined cell cycle genes based on cyclic variation in their expression. However, the concordance of the small number of synchronized cell studies has been limited, leading to discrepancies in definition of the transcriptionally regulated set of cell cycle genes within and between species. Here we present an informatics approach based on Boolean logic to identify cell cycle genes. This approach used the vast array of publicly available gene expression data sets to query similarity to CCNB1, which encodes the cyclin subunit of the Cdk1-cyclin B complex that triggers the G2-to-M transition. In addition to highlighting conservation of cell cycle genes across large evolutionary distances, this approach identified contexts where well-studied genes known to act during the cell cycle are expressed and potentially acting in nondivision contexts. An accessible web platform enables a detailed exploration of the cell cycle gene lists generated using the Boolean logic approach. The methods employed are straightforward to extend to processes other than the cell cycle.

scholarly journals GAAS: Gene Array Analyzer Software for management, analysis and visualization of gene expression data

2003 ◽  
Vol 19 (6) ◽  
pp. 774-775 ◽  
Author(s):  
M. Masseroli ◽  
P. Cerveri ◽  
P.G. Pelicci ◽  
M. Alcalay
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Array ◽  
Expression Data

scholarly journals Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration

Cells ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 786 ◽  
Author(s):  
Jingxin Tao ◽  
Youjin Hao ◽  
Xudong Li ◽  
Huachun Yin ◽  
Xiner Nie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Caenorhabditis Elegans ◽  
Gene Expression Data ◽  
Reference Genes ◽  
Large Scale ◽  
Gene Array ◽  
Housekeeping Genes ◽  
Functional Enrichment ◽  
Expression Data ◽  
Data Types

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (rps-23, rps-26, rps-27, rps-16, rps-2, rps-4, rps-17, rpl-24.1, rpl-27, rpl-33, rpl-36, rpl-35, and rpl-15) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans, for quantitative gene expression analysis.

UTILIZING EVOLUTIONARY INFORMATION AND GENE EXPRESSION DATA FOR ESTIMATING GENE NETWORKS WITH BAYESIAN NETWORK MODELS

2005 ◽  
Vol 03 (06) ◽  
pp. 1295-1313 ◽  
Author(s):  
YOSHINORI TAMADA ◽  
HIDEO BANNAI ◽  
SEIYA IMOTO ◽  
TOSHIAKI KATAYAMA ◽  
MINORU KANEHISA ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bayesian Network ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Expression Patterns ◽  
Supplementary Information ◽  
Evolutionary Information ◽  
Cell Cycle Gene ◽  
Sufficient Information ◽  
Expression Data

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at .

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