Secondary structure prediction methods attempts to infer the likely secondary\r
structure for a protein based on its amino acid composition and\r
similarity to sequences with known secondary structure. The JNet\r
-method uses several different neural metworks and decides on the\r
+method uses several different neural networks and decides on the\r
most likely prediction via a jury network. <br>\r
<ul>\r
<li>Cuff J. A and Barton G.J (1999) Application of enhanced multiple\r
structure prediction <em>Proteins</em> <strong>40</strong> 502-511</li></ul>\r
</p>\r
The function available from the <strong>Web Service→Secondary\r
-Structure Prediction→JNet Secondary Structure \r
-Prediction</strong> menu does two different kinds of prediction, \r
-dependent upon the currently selected region:</p> \r
+Structure Prediction→JNet Secondary Structure\r
+Prediction</strong> menu does two different kinds of prediction,\r
+dependent upon the currently selected region:</p>\r
<ul>\r
<li>If nothing is selected, and the displayed sequences appear to\r
be aligned, then a JNet prediction will be run for the first\r
then the alignment will be used for a Jnet prediction on the\r
<strong>first</strong> sequence selected in the set (that is, the one\r
nearest the top of the alignment window).\r
-</li> \r
+</li>\r
</ul>\r
<p>The result of a JNet prediction for a sequence is a new annotated\r
alignment window:</p>\r