1 Ocak 2021 Dergi makalesi Açık Erişim

Cagirici, H. Busra; Galvez, S.; Sen, Taner Z.; Budak, Hikmet

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/229882</identifier>
  <creators>
    <creator>
      <creatorName>Cagirici, H. Busra</creatorName>
      <givenName>H. Busra</givenName>
      <familyName>Cagirici</familyName>
    </creator>
    <creator>
      <creatorName>Galvez, S.</creatorName>
      <givenName>S.</givenName>
      <familyName>Galvez</familyName>
      <affiliation>Univ Malaga, ETSI Informat, Andalucia Tech, E-29071 Malaga, Spain</affiliation>
    </creator>
    <creator>
      <creatorName>Sen, Taner Z.</creatorName>
      <givenName>Taner Z.</givenName>
      <familyName>Sen</familyName>
      <affiliation>USDA ARS, Crop Improvement Genet Res Unit, Western Reg Res Ctr, 800 Buchanan St, Albany, CA 94710 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Budak, Hikmet</creatorName>
      <givenName>Hikmet</givenName>
      <familyName>Budak</familyName>
      <affiliation>Montana BioAgr Inc, Missoula, MT 59801 USA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Lncmachine: A Machine Learning Algorithm For Long Noncoding Rna Annotation In Plants</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/229882</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/s10142-021-00769-w</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">Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.</description>
  </descriptions>
</resource>