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Profiling Data (profiling + data)
Selected AbstractsGenomic imbalances in rhabdomyosarcoma cell lines affect expression of genes frequently altered in primary tumors: An approach to identify candidate genes involved in tumor developmentGENES, CHROMOSOMES AND CANCER, Issue 6 2009Edoardo Missiaglia Rhabdomyosarcomas (RMS) are the most common pediatric soft tissue sarcomas. They resemble developing skeletal muscle and are histologically divided into two main subtypes; alveolar and embryonal RMS. Characteristic genomic aberrations, including the PAX3 - and PAX7-FOXO1 fusion genes in alveolar cases, have led to increased understanding of their molecular biology. Here, we determined the effect of genomic copy number on gene expression levels through array comparative genomic hybridization (CGH) analysis of 13 RMS cell lines, confirmed by multiplex ligation-dependent probe amplification copy number analyses, combined with their corresponding expression profiles. Genes altered at the transcriptional level by genomic imbalances were identified and the effect on expression was proportional to the level of genomic imbalance. Extrapolating to a public expression profiling dataset for 132 primary RMS identified features common to the cell lines and primary samples and associations with subtypes and fusion gene status. Genes identified such as CDK4 and MYCN are known to be amplified, overexpressed, and involved in RMS tumorigenesis. Of the many genes identified, those with likely functional relevance included CENPF, DTL, MYC, EYA2, and FGFR1. Copy number and expression of FGFR1 was validated in additional primary material and found amplified in 6 out of 196 cases and overexpressed relative to skeletal muscle and myoblasts, with significantly higher expression levels in the embryonal compared with alveolar subtypes. This illustrates the ability to identify genes of potential significance in tumor development through combining genomic and transcriptomic profiles from representative cell lines with publicly available expression profiling data from primary tumors. © 2009 Wiley-Liss, Inc. [source] Multi-way models for sensory profiling dataJOURNAL OF CHEMOMETRICS, Issue 1 2008Rasmus Bro Abstract One of the problems in analyzing sensory profiling data is to handle the systematic individual differences in the assessments from different panelists. It is unavoidable that different persons have, at least to a certain degree, different perceptions of the samples as well as a different understanding of the attributes or of the scales used for quantifying the assessments. Hence, any model attempting to describe sensory profiling data needs to deal with individual differences; either implicitly or explicitly. In this paper, a unifying family of models is proposed based on (i) the assumption that latent variables are appropriate for sensory data, and (ii) that individual differences occur. Based on how individual differences occur, various mathematical models can be constructed, all aiming at modeling simultaneously the sample-specific variation and the panelist-specific variation. The model family includes Principal Component Analysis (PCA) and PARAllel FACtor analysis (PARAFAC). The paper can be viewed as extending the latent variable approach commonly based on PCA to multi-way models that specifically take certain panelist-variations into account. The proposed model family is focused on analyzing data from quantitative descriptive analysis with fixed vocabulary, but it also provides a foundation upon which comparisons, extensions and further developments can be made. An example is given which shows that even for well-working data, models handling individual differences can shed important light on differences between the quality of the data from individual panelists. Copyright © 2007 John Wiley & Sons, Ltd. [source] PERCEPTION OF CHEESE: A COMPARISON OF QUALITY SCORING, DESCRIPTIVE ANALYSIS AND CONSUMER RESPONSESJOURNAL OF FOOD QUALITY, Issue 4 2005MARGRETHE HERSLETH ABSTRACT The main objective of this study was to study perception of cheeses by comparing quality scores from expert assessors, descriptive profiling data from selected assessors and consumer responses. Twelve cheeses were evaluated by expert assessors and profiled by selected assessors. Five cheeses were selected for consumer testing and rated for hedonic liking, plus flavor intensity and degree of soft/firm texture. Analysis of variance and multivariate analyses of the data showed that the expert assessors scores for consistency, flavor and overall quality correlated positively with descriptive profiling attributes as mature flavor/odor, firmness, graininess and dryness of the cheeses. Preference mapping showed an even distribution of the consumers in the sensory map, which indicated different sensory segments. Some consumers preferred a firm cheese with a mild, mature flavor and others preferred a doughy cheese with more acid, fermented flavor. The expert assessors represented the preferences of the first group in their scoring procedure. [source] Assessing the value of an e-mail knowledge extraction systemKNOWLEDGE AND PROCESS MANAGEMENT: THE JOURNAL OF CORPORATE TRANSFORMATION, Issue 2 2009Sara Tedmori This paper reviews an approach to locating knowledge holders within organizations through the use of a well-established communication medium, E-mail. The approach has been used to develop the E-mail knowledge extraction (EKE) tool. EKE was then evaluated at an academic institution in the United Kingdom. This study represents the first effort to validate the viability of the E-mail medium as a source of knowledge profiling data, to be used for finding employees who possess the required knowledge. It also looks at the socio,ethical challenges associated with EKE's adoption. The overall evaluation of EKE found it to be useful, interesting, easy and intuitive to use and of potential benefit to employees within organizations. Copyright © 2009 John Wiley & Sons, Ltd. [source] Correlation-associated peptide networks of human cerebrospinal fluidPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 11 2005Jens Lamerz Dr. Abstract Profiling of peptides and small proteins from either human body fluids or tissues by chromatography and subsequent mass spectrometry reveals several thousand individual peptide signals per sample. Any peptide is an intermediate in the course of biosynthesis, post-translational modification (PTM), proteolytic processing and degradation. Changes in the concentration of one peptide often affects the concentration of the other, hence a challenge consists in the development of suitable tools to turn this large amount of data into biologically relevant information. Comprehensive statistical analysis of the peptide profiling data allows associating peptides, which are closely related in terms of peptide biochemistry. Here, the bioinformatic concept of peptide networks, correlation-associated peptide networks (CANs), is introduced. Peptides with statistical similarity of their concentrations are grouped in form of networks, and these networks are interpreted in terms of peptide biochemistry. The spectrum of functional relationships found in cerebrospinal fluid CAN covers PTM and proteolytic degradation of peptides, clearance processing in the complement cascade, common secretion of peptides by neuroendocrine cells as well as ubiquitin-mediated degradation. Our results indicate that CAN is a powerful bioinformatic tool for the systematic analysis and interpretation of large peptidomics and proteomics data and helps to discover novel bioactive and diagnostic peptides. [source] A systems biology investigation of the MEP/terpenoid and shikimate/phenylpropanoid pathways points to multiple levels of metabolic control in sweet basil glandular trichomesTHE PLANT JOURNAL, Issue 3 2008Zhengzhi Xie Summary The glandular trichome is an excellent model system for investigating plant metabolic processes and their regulation within a single cell type. We utilized a proteomics-based approach with isolated trichomes of four different sweet basil (Ocimum basilicum L.) lines possessing very different metabolite profiles to clarify the regulation of metabolism in this single cell type. Significant differences in the distribution and accumulation of the 881 highly abundant and non-redundant protein entries demonstrated that although the proteomes of the glandular trichomes of the four basil lines shared many similarities they were also each quite distinct. Correspondence between proteomic, expressed sequence tag, and metabolic profiling data demonstrated that differential gene expression at major metabolic branch points appears to be responsible for controlling the overall production of phenylpropanoid versus terpenoid constituents in the glandular trichomes of the different basil lines. In contrast, post-transcriptional and post-translational regulation of some enzymes appears to contribute significantly to the chemical diversity observed within compound classes for the different basil lines. Differential phosphorylation of enzymes in the 2- C -methyl- d -erythritol 4-phosphate (MEP)/terpenoid and shikimate/phenylpropanoid pathways appears to play an important role in regulating metabolism in this single cell type. Additionally, precursors for different classes of terpenoids, including mono- and sesquiterpenoids, appear to be almost exclusively supplied by the MEP pathway, and not the mevalonate pathway, in basil glandular trichomes. [source] Identification of novel heat shock factor-dependent genes and biochemical pathways in Arabidopsis thalianaTHE PLANT JOURNAL, Issue 1 2005Wolfgang Busch Summary In order to assess specific functional roles of plant heat shock transcription factors (HSF) we conducted a transcriptome analysis of Arabidopsis thaliana hsfA1a/hsfA1b double knock out mutants and wild-type plants. We used Affymetrix ATH1 microarrays (representing more than 24 000 genes) and conducted hybridizations for heat-treated or non-heat-treated leaf material of the respective lines. Heat stress had a severe impact on the transcriptome of mutant and wild-type plants. Approximately 11% of all monitored genes of the wild type showed a significant effect upon heat stress treatment. The difference in heat stress-induced gene expression between mutant and wild type revealed a number of HsfA1a/1b-regulated genes. Besides several heat shock protein and other stress-related genes, we found HSFA-1a/1b-regulated genes for other functions including protein biosynthesis and processing, signalling, metabolism and transport. By screening the profiling data for genes in biochemical pathways in which known HSF targets were involved, we discovered that at each step in the pathway leading to osmolytes, the expression of genes is regulated by heat stress and in several cases by HSF. Our results document that in the immediate early phase of the heat shock response HSF-dependent gene expression is not limited to known stress genes, which are involved in protection from proteotoxic effects. HsfA1a and HsfA1b-regulated gene expression also affects other pathways and mechanisms dealing with a broader range of physiological adaptations to stress. [source] Three-dimensional inversion of automatic resistivity profiling dataARCHAEOLOGICAL PROSPECTION, Issue 4 2009Nikos G. Papadopoulos Abstract Geophysical investigations through mobile multi-electrode systems, such as the automatic resistivity profiling (ARP) method, can increase the size of the surveyed areas without jeopardizing the spatial resolution of the survey. The representation of the apparent resistivity data in maps corresponding to the different measuring dipoles is sufficient in most routine applications for outlining the buried archaeological structures. In specific cases where a more quantitative interpretation of the apparent resistivity data is demanded, a three-dimensional resistivity inversion can provide the necessary tool for this purpose. This work investigates the possibilities and limitations of the three-dimensional resistivity inversion in processing the ARP data. A three-dimensional finite element smoothness-constrained inversion algorithm was used. The active constraint balancing (ACB) method was also applied in order to enhance the stability and the resolving power of the inversion procedure. Resistivity models that are commonly encountered in archaeological exploration were used to generate synthetic apparent resistivity data using a three-dimensional finite element forward modelling program. Inversion of the synthetic data showed that the maximum investigation depth of the ARP method is comparable to the length of the larger receiving dipole and cannot exceed the 2,2.5,m for the particular ARP device tested in this work. Archaeological structures buried within this depth range can be effectively mapped, while the resolution of the subsurface structures is related to the data acquisition parameters. The inversion algorithm was also used to reconstruct the three-dimensional resistivity distribution from the ARP data set collected from the Andilly archaeological site in France. The results effectively showed that the three-dimensional inversion can act as a complementary tool in acquiring a more quantitative interpretation model of the buried archaeological features. Copyright © 2009 John Wiley & Sons, Ltd. [source] Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease StageBIOMETRICS, Issue 3 2004Naijun Sha Summary Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis. [source] Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profilingCANCER SCIENCE, Issue 10 2003Tatsuya Ando A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http://llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937,47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases. [source] | |||||||||||||