Metabolic Engineering Correlations

Posted by Ellie on Oct 18, 2008 in Intelligent Systems for Molecular Biology | 0 comments

Guralnik Karypis G scalable algorithm for clustering protein sequences.

Motifs are scored with the attributes support, maxValue and infoBased. In the literature, many measures of interest and significance have been proposed. Since this categorization does not include the support as criterion to score the motifs improves the quality of the measures since all the respective correlations are smaller than Additional details for the use of these programs are provided whenever necessary. For each operation metainformation for motifs was generated according to the variables and values described in Table Journal of Computational Biology 52277304.

Evaluation performed for the prosite and synthetic datasets, and presented in Figure As this approach is only feasible for small and medium scale experiments, an alternative is to automatically evaluate motifs according to their statistical or informative importance. Deterministic motifs are described in regular expression based language, which tends to be easily understandable by humans. Dendogram for the measures with the three operations. PubMed Abstract These motifs can be divided in two types fixedlength and extensiblelength. The second case is consequence of the fact that both measures have small variation.

Hybrid Measures Considering measures that use both Informationtheoretic and class information. It can be seen from these two experiments that even for lower support values Support and IG still maintain clear advantage over the remaining measures. Figure Publisher Full Text Mulder Apweiler Attwood Bairoch Bateman Binns Bradley Bork Bucher Cerutti Copley Courcelle Das Durbin Fleischmann Gough J, Haft Harte Hulo Kahn Kanapin Krestyaninova Lonsdale Lopez The first factor, provides the prior probability of motif occurrence.

PAn1 Since the probability of the substring Ai. Since not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. In the same way, Sp will always show high scores due to large TN values. Closer distance in the tree represents higher consistency. From these results the following observations can be made Sn is highly consistent with Sp and PPV, which demonstrates that they are equally good replacing Sn, Sp and PPV when unique score value is required. Evaluation of contiguous motifs on Prosite data.