Monitoring gene expression using DNA microarrays

Harrington, Christina A.; Rosenow, Carsten; Retief, Jacques D.

doi:10.1016/s1369-5274(00)00091-6

Cited by 247 publications

(130 citation statements)

References 40 publications

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“…Proteins in gray boxes are unclassified (unknown function) while the others are classified proteins with the functions within the brackets and labeled according to the following criteria: 1-cell growth; 2-budding, cell polarity and filament formation; 3-pheromone response, mating-type determination, sex-specific proteins; 4-cell cycle check point proteins; 5-cytokinesis; 6-rRNA synthesis; 7-tRNA synthesis; 8-transcriptional control; 9-other transcription activities; 10-other pheromone response activities; 11-stress response; and 12-nuclear organization. Given one of these proteins of unknown function if we take as a prediction the function that appears more in the neighbor proteins of known function then we obtain the following classification (from top to bottom) YNL127W (2), YDR200C (3,4,10) and YLR238W (12). Our method, however, considers also the interactions among unclassified proteins.…”

Section: Figmentioning

confidence: 99%

“…In this context, the search for reliable methods for proteins' function assignment is of uttermost importance. Previous approaches to deduce the unknown function of a class of proteins have exploited sequence similarities or clustering of co-regulated genes [2,3], phylogenetic profiles [4], protein-protein interactions [5,6,7,8], and protein complexes [9,10]. We propose to assign functional classes to proteins from their network of physical interactions, by minimizing the number of interacting proteins with different categories.…”

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confidence: 99%

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Global protein function prediction from protein-protein interaction networks

et al. 2003

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Determining protein function is one of the most challenging problems of the post-genomic era. The availability of entire genome sequences and of high-throughput capabilities to determine gene coexpression patterns has shifted the research focus from the study of single proteins or small complexes to that of the entire proteome(1). In this context, the search for reliable methods for assigning protein function is of primary importance. There are various approaches available for deducing the function of proteins of unknown function using information derived from sequence similarity or clustering patterns of coregulated genes(2,3), phylogenetic profiles(4), protein-protein interactions (refs. 5-8 and Samanta, M. P. and Liang, S., unpublished data), and protein complexes(9,10). Here we propose the assignment of proteins to functional classes on the basis of their network of physical interactions as determined by minimizing the number of protein interactions among different functional categories. Function assignment is proteome-wide and is determined by the global connectivity pattern of the protein network. The approach results in multiple functional assignments, a consequence of the existence of multiple equivalent solutions. We apply the method to analyze the yeast Saccharomyces cerevisiae protein-protein interaction network(5). The robustness of the approach is tested in a system containing a high percentage of unclassified proteins and also in cases of deletion and insertion of specific protein interactions

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Section: Figmentioning

confidence: 99%

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confidence: 99%

Global protein function prediction from protein-protein interaction networks

et al. 2003

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“…Nearly 50 % of the open reading frames (ORFs) have no known function. Transcriptomics and proteomics (reviewed by Harrington et al, 2000;Cordwell et al, 2001) can be applied to gauge the expression of mRNA and protein geneproducts in response to altered genetic or biological conditions. Both approaches have recently been used to monitor the molecular basis of biofilm development in P. aeruginosa Sauer et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Proteome analysis of extracellular proteins regulated by the las and rhl quorum sensing systems in Pseudomonas aeruginosa PAO1

et al. 2003

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The las and rhl quorum sensing (QS) systems regulate the expression of several genes in response to cell density changes in Pseudomonas aeruginosa. Many of these genes encode surfaceassociated or secreted virulence factors. Proteins from stationary phase culture supernatants were collected from wild-type and P. aeruginosa PAO1 mutants deficient in one or more of the lasRI, rhlRI and vfr genes and analysed using two-dimensional gel electrophoresis. All mutants released significantly lower amounts of protein than the wild-type. Protein spot patterns from each strain were compared using image analysis and visible spot differences were identified using mass spectrometry. Several previously unknown QS-regulated proteins were characterized, including an aminopeptidase (PA2939), an endoproteinase (PrpL) and a unique 'hypothetical' protein (PA0572), which could not be detected in the culture supernatants of Dlas mutants, although they were unaffected in Drhl mutants. Chitin-binding protein (CbpD) and a hypothetical protein (PA4944) with similarity to host factor I (HF-I) could not be detected when any of the lasRI or rhlRI genes were disrupted. Fourteen proteins were present at significantly greater levels in the culture supernatants of QS mutants, suggesting that QS may also negatively control the expression of some genes. Increased levels of two-partner secretion exoproteins (PA0041 and PA4625) were observed and may be linked to increased stability of their cognate transporters in a QS-defective background. Known QS-regulated extracellular proteins, including elastase (lasB), LasA protease (lasA) and alkaline metalloproteinase (aprA) were also detected.

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“…DNA microarray technology has advanced so much that we can simultaneously measure the expression levels of thousands of genes under particular experimental environments and conditions [1]. DNA microarray technology makes it possible to understand life on the molecular level.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Ensemble with Negatively Correlated Features for Cancer Classification

Won

Cho

2003

Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

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Abstract. The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. In spite of many advantages of ensemble classifiers, ensemble with mutually errorcorrelated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features to classify cancer precisely, and systematically evaluate the performance of the proposed method using three benchmark datasets. Experimental results show that the neural network ensemble with negatively correlated features produces the best recognition rate on the three benchmark datasets.

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Monitoring gene expression using DNA microarrays

Cited by 247 publications

References 40 publications

Global protein function prediction from protein-protein interaction networks

Global protein function prediction from protein-protein interaction networks

Proteome analysis of extracellular proteins regulated by the las and rhl quorum sensing systems in Pseudomonas aeruginosa PAO1

Neural Network Ensemble with Negatively Correlated Features for Cancer Classification

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