Abstract </head> <body text="#000000" bgcolor="#FFFFFF" link="#0000EF" vlink="#CF30CF" alink="#FF0000" nosave> <br> <center><h2> Differential Weighted Gene Coexpression Network Analysis Applied to Mouse Weight </h2> <br> <h2> Tova Fuller </h2> <h3> (UC Los Angeles) </h3> </center> <br> Gene co-expression networks and subsequent single network analysis have been useful in identifying and characterizing biological interactions, and unraveling the regulation underlying higher organism complex traits. Here we present differential network analysis, a method for utilizing gene coexpression networks to compare and contrast genetic properties of distinct sample populations. We employ this method to study mouse weight using an F2 mouse intercross in which a weight-related module was previously identified. In comparing high and low weight mice, we demonstrate differential network structure, and furthermore examine the functional characteristics of genes comprising a highly differentially connected module. <br><br><br> <a href="index.html">Back to main page</a><br><br> <hr> <p><br><br>  <script type="text/javascript"> var sc_project=2800657; var sc_invisible=0; var sc_partition=28; var sc_security="dd1187b0"; </script> <script type="text/javascript" src="http://www.statcounter.com/counter/counter_xhtml.js"></script><noscript><div class="statcounter"><a class="statcounter" href="http://www.statcounter.com/"><img class="statcounter" src="http://c29.statcounter.com/2800657/0/dd1187b0/0/" alt="free stats" /></a></div></noscript>  </body> </htmle