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Table 1. Summary of results for selected motif candidates.

in Protein Motif Extraction with Neuro-Fuzzy Optimisation
by Bill C. H. Chang, Saman K. Halgamuge 2002
Cited by 2

Table 3 - Conserved motif counts and motif processing Conserved Motif counts

in unknown title
by unknown authors 2008
"... In PAGE 6: ... Strategy 2 uses all available alignment information (pairwise and multiple alignments) whereas strategy 3 does not use any alignment information in the actual motif discovery process. Table3 summarizes the different stages in the motif discovery process for each strategy.... In PAGE 7: ... We prune the list of motif candidates by removing degenerate motifs based on their Z-score and P-values. This step halves the number of motif candidates (see Table3 ). An overview of the entire processing pipeline is given in Figure 3.... ..."

(Table 2; Figure 3). These data strongly suggest that these gene clusters are part of the catabolite control regulon that is controlled by the central regulator CcpA. To further sub- stantiate this, a MAST-motif search was performed to identify putative CRE sites, for binding of CcpA [43,44], within the csc gene clusters and their upstream regions. Putative CRE sites could be identified for six out of the seven up-regulated csc clusters, generally upstream of the first gene of the cluster and in three clusters also inside csc genes (Figure 1, Table 3). In contrast, no significant CRE- like sites could be identified within or upstream of the residual csc gene clusters, supporting a functional role of the identified CRE-site candidate sequences in regulation of these clusters.

in BMC Genomics BioMed Central
by Jos Boekhorst, Lidia Muscariello, Douwe Molenaar, Michiel Kleerebezem 2006

Table 2. Numbers of candidates analyzed by the Apriori-like algorithm

in Discovering Frequent Episodes in Sequences of Complex Events
by Marek Wojciechowski
"... In PAGE 8: ... Table2 presents numbers of candidates analyzed by the Apriori-like algorithm for different numbers of event attributes (denoted as NATR) and their domain sizes ... ..."

Table 3. Most prevalent KDEL-like carboxy-terminal motifs of soluble kernel ER proteins.

in The Hera database and its use in the characterization of endoplasmic reticulum proteins
by M. Scott, et al. 2004
"... In PAGE 14: ...2% of the soluble proteins contain the signal. Table3 shows the most prevalent carboxy-terminal KDEL-like motifs in kernel ER proteins. Among the remaining 73.... ..."

Table 4. Accuracy of various weight matrix and HMM regimes for detecting AATAAA motifs

in DOI: 10.1093/nar/gkh656 A probabilistic model of 3 0 end formation in
by Ashwin Hajarnavis, Ian Korf, Richard Durbin 2004
"... In PAGE 5: ... Since cleavage sites are imprecise, we calculated the accuracy based on identifying the correct AATAAA motif and not the cleavage site. Table4 shows that a crude scan for all exact matches to AATAAA within 1000 nt of the stop codon cor- rectly identifies 56% of signals, though 46% of the total pre- dictions are spurious. If we propose that the 50-most exact match to AATAAA is the signal, the proportion of signals detected correctly is reduced by 5% but there is an 8% increase in specificity.... In PAGE 6: ... For each gene, we used our HMM to search the 1000 bases 30 of each annotated stop codon, and annotated the most likely cleavage site as deter- mined by the Viterbi algorithm (available in the supplement- ary data). We expect 70% of these to be correct, from our previous experiments ( Table4 ). Figure 4 shows the frequency distribution of the distance between WormBase 30-UTRs and the Viterbi prediction for each of their 30-UTR candidates.... In PAGE 7: ... In these experi- ments, we modified the HMM by including 3 coding states which correspond to the nucleotide frequencies observed in first, second and third positions within codons. Table4 shows that the weight matrix methods find a large number of false positives in the coding sequence. However, the specificity of the HMM degrades only slightly, and the performance differ- ence of the posterior decoding is particularly small.... ..."

Table 3.2: Ability of the model to learn motifs that are as likely as the true structure in synthetic data.

in Learning Probabilistic Relational Models in the context of reverse engineering genetic
by Christoforos Anagnostopoulos

Table 1. Nucleotide substitution rates in motifs

in
by Brian T. Naughton, Eugene Fratkin, Serafim Batzoglou, Douglas L. Brutlag 2006
"... In PAGE 6: ... Specifically, we calculated the likelihood of each nucleotide substituting for every other in each motif in the dataset. These frequencies are shown in Table1 . For details of how the table was generated see Materials and Methods.... In PAGE 7: ... QNS(b1,b2) (the nucleotide substitution parameter) is the appropriate value from our nucleotide substitution matrix, where b1 (the nucleotide in the candidate k-mer) is substitu- ting for b2 (the nucleotide in the motif k-mer). If d is greater than 1, then QNS(b1,b2) is the average of all of the substitution probabilities (given in Table1 , a and c). Pseudo-k-mers.... ..."

Table 1. Nucleotide substitution rates in motifs

in
by Brian T. Naughton, Eugene Fratkin, Serafim Batzoglou, Douglas L. Brutlag 2006
"... In PAGE 6: ... Specifically, we calculated the likelihood of each nucleotide substituting for every other in each motif in the dataset. These frequencies are shown in Table1 . For details of how the table was generated see Materials and Methods.... In PAGE 7: ... QNS(b1,b2) (the nucleotide substitution parameter) is the appropriate value from our nucleotide substitution matrix, where b1 (the nucleotide in the candidate k-mer) is substitu- ting for b2 (the nucleotide in the motif k-mer). If d is greater than 1, then QNS(b1,b2) is the average of all of the substitution probabilities (given in Table1 , a and c). Pseudo-k-mers.... ..."

Table 1. Nucleotide substitution rates in motifs

in
by Brian T. Naughton, Eugene Fratkin, Serafim Batzoglou, Douglas L. Brutlag 2006
"... In PAGE 6: ... Specifically, we calculated the likelihood of each nucleotide substituting for every other in each motif in the dataset. These frequencies are shown in Table1 . For details of how the table was generated see Materials and Methods.... In PAGE 7: ... QNS(b1,b2) (the nucleotide substitution parameter) is the appropriate value from our nucleotide substitution matrix, where b1 (the nucleotide in the candidate k-mer) is substitu- ting for b2 (the nucleotide in the motif k-mer). If d is greater than 1, then QNS(b1,b2) is the average of all of the substitution probabilities (given in Table1 , a and c). Pseudo-k-mers.... ..."
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