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Table 1. Network composed by cis-regulatory elements. Gene Regulators
Table 3. Network inferred by pruning the cis-regulatory elements. Gene Regulators
"... In PAGE 7: ... Again, we also computed the network, which was obtained by just pruning the initial network network without introducing any further interactions. It had only 9 genes with a nonempty regulator set and is shown in Table3 . Like in the last subsection, in Table 4 we compared the resulting average 5-fold cross-validated mean squared correla- tions for these genes with those obtained from the cis-regulatory, the random and the fully connected network.... ..."
Table 4 Pre-defined regulatory network models for simulation Network
"... In PAGE 10: ... Based on the factors described above, three gene regulatory models were generated using a random procedure. The key factors of the three models are presented in Table4 . The three network models (G1, G2, and G3) contained 10, 50, and 200 nodes, respectively.... ..."
Table 5. 5-fold cross-validated correlations (r) with true expression levels for genes with nonempty regulator set (cis-regulatory elements). For the inferred networks (pruned+modification, pruned only) the p-values are calculated as described in subsec- tion 2.3.
Table 5. 5-fold cross-validated correlations (r) with true expression levels for genes with nonempty regulator set (cis-regulatory elements). For the inferred networks (pruned+modification, pruned only) the p-values are calculated as described in subsec- tion 2.3.
Table 3. Network inferred by pruning the cis-regulatory elements.
"... In PAGE 7: ... Again, we also computed the network, which was obtained by just pruning the initial network network without introducing any further interactions. It had only 9 genes with a nonempty regulator set and is shown in Table3 . Like in the last subsection, in Table 4 we compared the resulting average 5-fold cross-validated mean squared correla- tions for these genes with those obtained from the cis-regulatory, the random and the fully connected network.... ..."
Table 4. Network inferred by pruning the cis-regulatory elements network and some allowed subsequent modifications. Regulators introduced addi- tionally to the pruned network are marked bold. Regulators, which were not in the a priori network are written italic as well. Gene Regulators
"... In PAGE 7: ....2. Cis-Regulatory Elements Our method from subsection 2.2 on this dataset yielded 11 genes with a nonempty regulator set ( Table4 ). Again, we also computed the network, which was obtained by just pruning the initial network network without introducing any further interactions.... In PAGE 7: ... It had only 9 genes with a nonempty regulator set and is shown in Table 3. Like in the last subsection, in Table4 we compared the resulting average 5-fold cross-validated mean squared correla- tions for these genes with those obtained from the cis-regulatory, the random and the fully connected network. As seen, the pruned network with additional modifications yielded a high significant improvement compared to the random, the cis-regulatory network and the... ..."
Table 1 describes the object types in the representation; Figure 1 illustrates the rep- resentation. Figure 2 provides an overview of the algorithm used to develop a phe- notype from a genotype. Note how most of the dynamics rely on the interaction of fractal proteins. Evolution is used to design genes that are expressed into fractal proteins with specific shapes, which result in developmental processes with specific dynamics.
2003
"... In PAGE 2: ... The result is an emergent computer program made from dy- namically forming gene regulatory networks (GRNs) that control all cell growth, position and behaviour in a developing creature [13]. Table1 . Types of objects in the representation fractal proteins defined as subsets of the Mandelbrot set.... ..."
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Table 2. Number of extreme pathways which each regulatory flow is associated with
"... In PAGE 6: ... It can provide information to assess the func- tion of the gene. To identify key components of the network structure and evaluate the relative importance of the gene in the network, we present Table2 which lists the number of extreme pathways that each regulatory flow involves. From Table 2, we can see that the total number of extreme path- ways, which the flows from gene Fas to gene FADD and from TRADD to FADD are part of, is 44 and accounts for 81.... In PAGE 6: ... To identify key components of the network structure and evaluate the relative importance of the gene in the network, we present Table 2 which lists the number of extreme pathways that each regulatory flow involves. From Table2 , we can see that the total number of extreme path- ways, which the flows from gene Fas to gene FADD and from TRADD to FADD are part of, is 44 and accounts for 81.48% of the total extreme regulatory pathways of major apoptosis process.... ..."
Table 1 How do we come from threshold values to the corresponding genes? So, if we found some thresholds between data points, the question about which genes are responsible for these thresholds arises. In order to decide this, we have to look at the networks that are de ned through our matrices. Within two thresholds we have to solve the optimization problem (2) with constraints. This leads to matrices Ms(t), which de ne regulatory networks, and vectors ks(t) for any region between thresholds, that provides additional information we need to decide which genes are responsible for the thresholds. We want to illustrate this in Figure 6 with a network of three genes.
"... In PAGE 17: ...Figure 6. Arti cial network with three interacting genes Furthermore, in Table1 , we set all parameters of the network, i.e.... ..."
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