P. D'Haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering, " Bioinformatics, vol. 16, no. 8, pp. 707--726, 2000. Home/Search Document Not in Database Summary Related Articles Check |
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On the Complete Determination of Biological Systems - Selinger, Wright, Church (Correct)
....e.g. descriptions of the cell s environment) to a set of outputs (see Fig. 1, yellow area; e.g. the concentration of all of its RNAs) Large scale experimental sampling of input output pairs (Fig. 1, yellow red dots) such as condition transcriptome pairs, can be used to derive these rules [3]. To specify a particular model we must decide on its basic properties (Table 1) First, we must decide on the inputs and outputs. This choice will depend, for example, on whether we are interested in predicting transcriptomes from temperature and pH, or in predicting successive molecular states. ....
....required ,30 40 million sequencing reads [11,12] a number that was not practical at the time the project was first proposed. There are other methods for inferring rules directly from large scale datasets and for estimating the number of measurements necessary for a given level of accuracy [3,13,14]. Additionally, current microbial models based on flux balance analysis have shown considerable progress towards a complete description of metabolism, with mappings from culture conditions and genotype (input) to growth phenotype (output) that reach accuracies .90 (106 116) 15] Models of this ....
D'haeseleer, P. et al. (2000) Genetic network inference: from coexpression clustering to reverse engineering. Bioinformatics 16,
Cluster Analysis for Gene Expression Data: A Survey - Jiang, Zhang (2002) (4 citations) (Correct)
.... environment or across different tissue types provides a systematic genome wide approach to solve the problems such as gene functions in various cellular process, gene regulations in various cellular signaling pathways and gene expression differentiation in various diseases or drug treatments [Brazma2000, D haeseleer2000, Sharan2001]. In answer to all those questions, clustering technique manifests its crucial power as the first step in extracting information from the mass of gene expression data set. Clustering algorithms have proved useful to help group together genes with similar functions based on gene expression ....
P. D'haeseleer, S. Liang, and R. Somogyi. "Genetic network inference: from co-expression clustering to reverse engineering", Bioinformatics, Vol. 16(8):707-726, 2000.
Searching for Limited Connectivity in Genetic Network.. - van Someren, Wessels.. (2001) (Correct)
....values; 2) how dynamic behavior is modeled, i.e. discrete or continuoustime systems and 3) what kind of relationships between genes are allowed, i.e. lattice, linear or nonlinear functions. An extensive comparison between the di#erent models is beyond the scope of this paper, more can be found in [7, 6]. In [22, 17] we presented comparative studies with experimental investigations. Here, we will su#ce by summarizing some properties that reflect the complexity of these models (See Table 1) Because it is important to be able to learn large networks, i.e. with more than 100 genes, the model ....
P. D'Haeseleer, S. Liang, and R. Somogyi. Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics, 16(8):707--726, 2000.
Unknown - Multivariate Analysis Multivariate (Correct)
....rather than numerical data, Hamming or Levenshtein distances can be used (these are important, for instance, in comparison of gene or protein sequences) Mutual Information is a similarity measure based on Shannon entropy (see Section 3.1. 1) that has been used for clustering gene expression data [5]. Alternatively, one can compare pairs of items based on the presence or absence of certain characteristics, with similar items having more characteristics in common than dissimilar items. For example, one could define a set of binary characteristics such as whether a given gene s expression is ....
P. D'Haeseleer, S. Liang, and R. Somogyi, Genetic network inference: From co-expression clustering to reverse engineering, Bioinformatics 16 (2000), no. 8, 707--726.
Modeling Genetic Network by Hybrid GP - Shin Ando Erina (Correct)
....network model parameterize degree of affection between the genes in znatrix form. Tile simplest form of is the Boolean networks [12] which approxinate the gene expression to on and off . Recent focus of the study has shifted to continuous real number framework, such as linear models (eq. 1) 5] 16] and power law models (eq. 2) 13] Eq. 1) represent a general linear model, which approximate the combined effect of the different gene inputs by means of weighed sum of their expression levels. The power law model(Eq. 2) can model much more complicated behavior in mRNAs, proteins, and ....
....Following are the summarized steps for LMS GP: 1. Initialize: create the populations 2. Derive the fitness according to (eq. 4) 3. Generate the trees of the next generation by selection and genetic recombination 4. Calculate the coefficients of the items in the trees by using tile LMS method 5. Go to 2 The terminal set T and the function set F are described in eq. 3) A table of coefficients, along with the tree, composes a GP individual as the coefficients are not included in the terminal set. The model numerically calculates the time series using the fourth order Runge Kutta ....
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D'Haeseleer, P., S. LiangR. Somogyi (2000). "Genetic network inference: from co-expression clustering to reverse engineering
Reduction Mappings between Probabilistic Boolean Networks - Ivanov, Dougherty (2004) (Correct)
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P. D'Haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering, " Bioinformatics, vol. 16, no. 8, pp. 707--726, 2000.
Gene Prediction Using Multinomial Probit Regression with.. - Zhou, al. (2004) (Correct)
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P. D'haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering, " Bioinformatics, vol. 16, no. 8, pp. 707--726, 2000.
The π-Calculus as an Abstraction for Biomolecular Systems - Regev, Shapiro (Correct)
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P. Dhaeseleer, S. Liang, and R. Somogyi. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 16(8):707--726, 2000.
Construction of Genetic Network Using Evolutionary Algorithm and .. - Shin, Iba (2003) (Correct)
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D'Haeseleer, P., Liang, S., and Somogyi, R., Genetic network inference: from co-expression clustering to reverse engineering, Bioinformatics, 16(8):707--726., 2000.
Reduction Mappings between Probabilistic Boolean Networks - Ivanov, al. (2004) (Correct)
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P. D'Haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering, " Bioinformatics, vol. 16, no. 8, pp. 707--726, 2000.
Gene Prediction Using Multinomial Probit Regression with.. - Zhou, al. (2004) (Correct)
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P. D'haeseleer, S. Liang, and R. Somogyi, "Genetic network inference: from co-expression clustering to reverse engineering, " Bioinformatics, vol. 16, no. 8, pp. 707--726, 2000.
Global Qualitative Description of a Class of Nonlinear.. - Bernard, Gouzé (2002) (1 citation) (Correct)
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P. D'Haeseleer, S. Liang, R. Somogyi, Genetic network inference: From co-expression clustering to reverse engineering, Bioinformatics 16 (2000) 707--726.
Detecting Stable Clusters Using Principal Component Analysis - Ben-Hur, Guyon (Correct)
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D'haeseleer, P., Liang, S. and Somogyi, R. (2000) Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics, 16, 707--726.
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