7 Haziran 2024 Konferans bildirisi Açık Erişim

Köse, Utku; Deperlioğlu, Ömer; Küçüksille, Ecir Uğur; Turan, Gökhan

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/274337</identifier>
  <creators>
    <creator>
      <creatorName>Köse, Utku</creatorName>
      <givenName>Utku</givenName>
      <familyName>Köse</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9652-6415</nameIdentifier>
      <affiliation>Süleyman Demirel Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Deperlioğlu, Ömer</creatorName>
      <givenName>Ömer</givenName>
      <familyName>Deperlioğlu</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7241-5219</nameIdentifier>
      <affiliation>Afyon Kocatepe Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Küçüksille, Ecir Uğur</creatorName>
      <givenName>Ecir Uğur</givenName>
      <familyName>Küçüksille</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3293-9878</nameIdentifier>
      <affiliation>Süleyman Demirel Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Turan, Gökhan</creatorName>
      <givenName>Gökhan</givenName>
      <familyName>Turan</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9698-8986</nameIdentifier>
      <affiliation>Burdur Mehmet Akif Ersoy Üniversitesi</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Combining Deep Learning Models For Improved Drug Repurposing: Advancements And An Extended Solution Methodology</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2024</publicationYear>
  <subjects>
    <subject>yapay zeka</subject>
    <subject>derin öğrenme</subject>
    <subject>ilaç yeniden konumlandırma</subject>
    <subject>karar destek sistemleri</subject>
    <subject>artificial intelligence</subject>
    <subject>deep learning</subject>
    <subject>drug repurposing</subject>
    <subject>decision support systems</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2024-06-07</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/274337</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/ICICT60155.2024.10544998</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Nowadays, major advancements through Artificial Intelligence (AI) were led by Deep Learning-based solutions. Considering their robust and extensive data processing mechanisms, Deep Learning (DL) models ensure great role in advancing solutions for real-world problems. Especially medical applications have been significantly improved by research studies as a result of intensive DL synergy. At this point, drug discovery has been one of the most remarkable fields where DL has been used in especially last few years. In the context of drug discovery studies, drug repurposing has a unique place to enable known drugs to be used for different diseases. As this is a remarkable way of optimizing discovery and treatment phases, use of DL for drug repurposing applications has still open areas to go. Objective of this paper is to examine the potential of combined DL models for improving drug repurposing and introduce a solution methodology, which includes use of multiple DL models to build a decision support system. It has been also aimed to support the system with computational models and Generative AI route to extend the capabilities towards a Digital Twin related approach.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/501100004410</funderIdentifier>
      <awardNumber>123E383</awardNumber>
    </fundingReference>
  </fundingReferences>
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