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	<title>ISMB 2008 &#187; motif structure</title>
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		<title>When unique value is required to</title>
		<link>http://www.ismb2008.org/when-unique-value-is-required-to/</link>
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		<pubDate>Sat, 18 Apr 2009 10:06:28 +0000</pubDate>
		<dc:creator>Ellie</dc:creator>
				<category><![CDATA[Intelligent Systems for Molecular Biology]]></category>
		<category><![CDATA[algorithms]]></category>
		<category><![CDATA[amp]]></category>
		<category><![CDATA[motif structure]]></category>

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		<description><![CDATA[Finally, the score values for the different measures are in accordance, but lead to difficulties in identifying the interesting motifs when disagreements are verified. characteristic of classbased measures is that they do not rely on the motif structure to be calculated. Both criteria are complementary in the task of automatically retrieving significant motifs from database. [...]]]></description>
			<content:encoded><![CDATA[<p>Finally, the score values for the different measures are in accordance, but lead to difficulties in identifying the interesting motifs when disagreements are verified. characteristic of classbased measures is that they do not rely on the motif structure to be calculated. Both criteria are complementary in the task of automatically retrieving significant motifs from database. In the work of Tan, Kumar and Srivastava 14, survey and general evaluation of itemset interest measures is presented. Variability analysis of the fourteen significance measures for four Prosite family entries PS00978, PS001172, PS00076 and PS00021.</p>
<p>This result is good example that significance measures can be replaced by others without lost of information. Figure shows the correlation matrix for the measures. This problem is different from the motif evaluation problem, since an item occurs only once per itemset, which is not the case of motifs, where an item called symbol occur repeatedly. Measures that provide larger variability will allow an easier discrimination between high scoring motifs. Dark areas indicate high correlation, and according to Definition higher consistency.</p>
<p>The number of evaluated motifs and the sources where the evaluated data is obtained. This result is good example that significance measures can be used as clever mechanism to prune motifs not only after, but also before, their significance is computed. This function returns real value score that expresses how relevant or significant is with respect to The last measure used was the ZScore measure. Each measure is associated to vector of values 1000 and an allagainstall vector comparison is made with the respective correlation being calculated. The file Prosite.</p>
<p>Significance measures are then introduced according to the variables and values described in Table Variability analysis of the fourteen significance measures for four Prosite family entries. The SwissProt database release 49. 0 was used as the negative information. Different types of motifs representation have been proposed and two main classes can be distinguished probabilistic and deterministic. It is interesting to note that these last three measures provide very similar results and that Pratt also has reasonable results.</p>
<p>Different types of motifs is evaluated. Since this database is considered standard, new algorithms and methods tend to use it as benchmark testbed. As introduced by Brazma et al. The second part is dedicated to the experimental evaluation. 16, an assessment of popular algorithms for the discovery of TFBS was performed. demonstrated that  log2 is the saving obtained from motif over covered sequences, which is equivalent to the IG formula. The Pratt Pratt measure was introduced by Jonassen et al.</p>
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