<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Huzefa Rangwala</style></author><author><style face="normal" font="default" size="100%">George Karypis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">fRMSDPred: predicting local RMSD between structural fragments using sequence information.</style></title><secondary-title><style face="normal" font="default" size="100%">Comput Syst Bioinformatics Conf</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemical</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Least-Squares Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">Peptide Fragments/*chemistry</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Conformation</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Folding</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein/*methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins/*chemistry/*ultrastructure</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Surface Properties</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pub-location><style face="normal" font="default" size="100%">Computer Science &amp; Engineering, University of Minnesota, Minneapolis, MN 55455, USA. rangwala@cs.umn.edu</style></pub-location><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">311–322</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The effectiveness of comparative modeling approaches for protein structure prediction can be substantially improved by incorporating predicted structural information in the initial sequence-structure alignment. Motivated by the approaches used to align protein structures, this paper focuses on developing machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel functions. Our comprehensive empirical study shows superior results compared to the profile-to-profile scoring schemes.</style></abstract></record></records></xml>