共查询到20条相似文献,搜索用时 15 毫秒
1.
Shuhei Kimura Katsuki Sonoda Soichiro Yamane Hideki Maeda Koki Matsumura Mariko Hatakeyama 《BMC bioinformatics》2008,9(1):23
Background
The inference of a genetic network is a problem in which mutual interactions among genes are deduced using time-series of gene expression patterns. While a number of models have been proposed to describe genetic regulatory networks, this study focuses on a set of differential equations since it has the ability to model dynamic behavior of gene expression. When we use a set of differential equations to describe genetic networks, the inference problem can be defined as a function approximation problem. On the basis of this problem definition, we propose in this study a new method to infer reduced NGnet models of genetic networks. 相似文献2.
3.
Malik Yousef Mohamed Ketany Larry Manevitz Louise C Showe Michael K Showe 《BMC bioinformatics》2009,10(1):337
Background
Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes. 相似文献4.
Franck Rapaport Andrei Zinovyev Marie Dutreix Emmanuel Barillot Jean-Philippe Vert 《BMC bioinformatics》2007,8(1):35
Background
Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. 相似文献5.
Robert Sidney CoxIII Mary J Dunlop Michael B Elowitz 《Journal of biological engineering》2010,4(1):10
Background
Current methods for analyzing the dynamics of natural regulatory networks, and quantifying synthetic circuit function, are limited by the lack of well-characterized genetic measurement tools. Fluorescent reporters have been used to measure dynamic gene expression, but recent attempts to monitor multiple genes simultaneously in single cells have not focused on independent, isolated measurements. Multiple reporters can be used to observe interactions between natural genes, or to facilitate the 'debugging' of biologically engineered genetic networks. Using three distinguishable reporter genes in a single cell can reveal information not obtainable from only one or two reporters. One application of multiple reporters is the use of genetic noise to reveal regulatory connections between genes. Experiments in both natural and synthetic systems would benefit from a well-characterized platform for expressing multiple reporter genes and synthetic network components. 相似文献6.
Zhu-Mei He Michael S Price Gregory R OBrian D Ryan Georgianna Gary A Payne 《BMC microbiology》2007,7(1):104
Background
An available whole genome sequence for Aspergillus flavus provides the opportunity to characterize factors involved in pathogenicity and to elucidate the regulatory networks involved in aflatoxin biosynthesis. Functional analysis of genes within the genome is greatly facilitated by the ability to disrupt or mis-express target genes and then evaluate their result on the phenotype of the fungus. Large-scale functional analysis requires an efficient genetic transformation system and the ability to readily select transformants with altered expression, and usually requires generation of double (or multi) gene deletion strains or the use of prototrophic strains. However, dominant selectable markers, an efficient transformation system and an efficient screening system for transformants in A. flavus are absent. 相似文献7.
8.
Quigley DA To MD Kim IJ Lin KK Albertson DG Sjolund J Pérez-Losada J Balmain A 《Genome biology》2011,12(1):R5
Background
Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown. 相似文献9.
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Background
Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type. 相似文献12.
Background
Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure. 相似文献13.
14.
Ting Gong Jianhua Xuan Li Chen Rebecca B Riggins Huai Li Eric P Hoffman Robert Clarke Yue Wang 《BMC bioinformatics》2011,12(1):82
Background
Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated. 相似文献15.
Daniela Nitsch Joana P Gonçalves Fabian Ojeda Bart de Moor Yves Moreau 《BMC bioinformatics》2010,11(1):460
Background
Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals. 相似文献16.
Background
The genetic architecture of a quantitative trait influences the phenotypic response to natural or artificial selection. One of the main objectives of genetic mapping studies is to identify the genetic factors underlying complex traits and understand how they contribute to phenotypic expression. Presently, we are good at identifying and locating individual loci with large effects, but there is a void in describing more complex genetic architectures. Although large networks of connected genes have been reported, there is an almost complete lack of information on how polymorphisms in these networks contribute to phenotypic variation and change. To date, most of our understanding comes from theoretical, model-based studies, and it remains difficult to assess how realistic their conclusions are as they lack empirical support. 相似文献17.
Background
The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively co-regulated groups, as opposed to those that are simply co-expressed. Be cause genes may be co-regulated only across a subset of all observed experimental conditions, biclustering (clustering of genes and conditions) is more appropriate than standard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically, and/or evolutionarily) associated, and such a priori known or pre-computed associations can provide support for appropriately grouping genes. One important association is the presence of one or more common cis-regulatory motifs. In organisms where these motifs are not known, their de novo detection, integrated into the clustering algorithm, can help to guide the process towards more biologically parsimonious solutions. 相似文献18.
Background
Presence of all three ParaHox genes has been described in deuterostomes and lophotrochozoans, but to date one of these three genes, Xlox has not been reported from any ecdysozoan taxa and both Xlox and Gsx are absent in nematodes. There is evidence that the ParaHox genes were ancestrally a single chromosomal cluster. Colinear expression of the ParaHox genes in anterior, middle, and posterior tissues of several species studied so far suggest that these genes may be responsible for axial patterning of the digestive tract. So far, there are no data on expression of these genes in molluscs. 相似文献19.