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        <title>Article Comments - 'pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model'</title>
        <link>http://www.biology-direct.com/content/2/1/1/comments</link>
        <description>The latest comments on the article 'pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model'</description>
        <dc:date>2007-06-18T11:37:44Z</dc:date>
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        <title>Is pkaPS the best predictor for PKA phosphorylation sites prediction?</title>
        <link>http://www.biology-direct.com/content/2/1/1/comments#285539</link>
        <description>&lt;p&gt;When I browse the PubMed for literature of phosphorylation sites prediction, I find this interesting article. Articles from Dr. Frank Eisenhaber&apos;s lab are always interesting and insighful. And the analysis in the article is comprehensive without tedious.&lt;/p&gt;&lt;p&gt;As a major author of GPS (Group-based phosphorylation scoring method), I am thankful the authors appreciated our and others (e.g., NETPHOSK, SCANSITE and etc.) work justly and impartially. However, I should point out something for futher arguement. &lt;/p&gt;&lt;p&gt;In Table 3 of pkaPS article, the values of Sn &amp;#38; Sp of SCANSITE, GPS and PREDPHOSPHO are taken from the published articles (Ref 14-16) rather than re-calculation. Acturally, the positive &amp;#38; negative control data sets are different at least in GPS and PREDPHOSPHO. For example, in Ref15(GPS article), the Sn &amp;#38; Sp of  PREDPHOSPHO are re-calculated with the same data set for GPS, and the values are 93.60% &amp;#38; 91.34%. But in Kim 2004 article (Ref 16), the Sn &amp;#38; Sp are 88.3% &amp;#38; 91.1%. I have read the pkaPS article. The positive &amp;#38; negative data sets are different with GPS &amp;#38; PREDPHOSPHO. Thus, the comparison may be a little bit biased. Thus, if pkaPS is claimed to be the best predictor for PKA phosphorylation sites prediction, all algorithm might be employed and applied to the same data sets. &lt;/p&gt;&lt;p&gt;Again, the article is still interesting and important. For example, the peptide used in the article is much longer than previous (-18,+23), about 42aa. This longer peptide provided more information for PKA phosphorylation sites prediction. &lt;/p&gt;&lt;p&gt;In my experience, it&apos;s hard to say which predictor is the best. During the data accumlated and algorithms improved, I believe the authors of pkaPS, we and other colleages focused on computational PTMs will propel the PTMs sites prediction into a new phase.&lt;/p&gt;</description>
                <dc:creator>Yu Xue</dc:creator>
                <dc:date>2007-06-18T11:37:44Z</dc:date>
        <prism:references>http://www.biology-direct.com/content/2/1/1</prism:references>
        <prism:person>Neuberger et al.</prism:person>
        <prism:publicationName>Biology Direct</prism:publicationName>
        <prism:volume>2</prism:volume>
        <prism:startingPage>1</prism:startingPage>
        <prism:publicationDate>Fri Jan 12 16:04:32 GMT 2007</prism:publicationDate>
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