1 Ocak 2017 Konferans bildirisi Açık Erişim

Aydin, Zafer; Uzut, Ommu Gulsum

DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/52193</identifier>
  <creators>
    <creator>
      <creatorName>Aydin, Zafer</creatorName>
      <givenName>Zafer</givenName>
      <familyName>Aydin</familyName>
      <affiliation>Abdullah Gul Univ, Kayseri, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Uzut, Ommu Gulsum</creatorName>
      <givenName>Ommu Gulsum</givenName>
      <familyName>Uzut</familyName>
      <affiliation>Mus Alparslan Univ, Mus, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Combining Classifiers For Protein Secondary Structure Prediction</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2017</publicationYear>
  <dates>
    <date dateType="Issued">2017-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/52193</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/CICN.2017.9</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Protein secondary structure prediction is an important step in estimating the three dimensional structure of proteins. Among the many methods developed for predicting structural properties of proteins, hybrid classifiers and ensembles that combine predictions from several models are shown to improve the accuracy rates. In this paper, we train, optimize and combine a support vector machine, a deep convolutional neural field and a random forest in the second stage of a hybrid classifier for protein secondary structure prediction. We demonstrate that the overall accuracy of the proposed ensemble is comparable to the success rates of the state-of-the-art methods in the most difficult prediction setting and combining the selected models have the potential to further improve the accuracy of the base learners.</description>
  </descriptions>
</resource>