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<export-example>
  <doc>
    <id>1800</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName>Frontiers</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action</title>
    <abstract language="eng">The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.</abstract>
    <parentTitle language="eng">Frontiers in Microbiology</parentTitle>
    <identifier type="issn">1664-302X</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-18004</identifier>
    <enrichment key="opus.import.date">2023-09-25T07:13:41+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="SourceTitle">D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau EC-dS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol E-S, Truică C-O, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ and Claesson MJ (2023) Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front. Microbiol. 14:1257002. doi: 10.3389/fmicb.2023.1257002</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3389/fmicb.2023.1257002</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Domenica D'Elia</author>
    <author>Jaak Truu</author>
    <author>Leo Lahti</author>
    <author>Magali Berland</author>
    <author>Georgios Papoutsoglou</author>
    <author>Michelangelo Ceci</author>
    <author>Aldert Zomer</author>
    <author>Marta B. Lopes</author>
    <author>Eliana Ibrahimi</author>
    <author>Aleksandra Gruca</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Thomas Klammsteiner</author>
    <author>Enrique Carrillo de Santa Pau</author>
    <author>Laura Judith Marcos-Zambrano</author>
    <author>Karel Hron</author>
    <author>Gianvito Pio</author>
    <author>Andrea Simeon</author>
    <author>Ramona Suharoschi</author>
    <author>Isabel Moreno-Indias</author>
    <author>Andriy Temko</author>
    <author>Miroslava Nedyalkova</author>
    <author>Elena-Simona Apostol</author>
    <author>Ciprian-Octavian Truică</author>
    <author>Rajesh Shigdel</author>
    <author>Jasminka Hasić Telalović</author>
    <author>Erik Bongcam-Rudloff</author>
    <author>Piotr Przymus</author>
    <author>Naida Babić Jordamović</author>
    <author>Laurent Falquet</author>
    <author>Sonia Tarazona</author>
    <author>Alexia Sampri</author>
    <author>Gaetano Isola</author>
    <author>David Pérez-Serrano</author>
    <author>Vladimir Trajkovik</author>
    <author>Lubos Klucar</author>
    <author>Tatjana Loncar-Turukalo</author>
    <author>Aki S. Havulinna</author>
    <author>Christian Jansen</author>
    <author>Randi J. Bertelsen</author>
    <author>Marcus Joakim Claesson</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>microbiome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>standard</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>best practice</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1800/fmicb-14-1257002.pdf</file>
  </doc>
  <doc>
    <id>1827</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName>Frontiers</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A toolbox of machine learning software to support microbiome analysis</title>
    <abstract language="eng">The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.</abstract>
    <parentTitle language="eng">Frontiers in Microbiology</parentTitle>
    <identifier type="issn">1664-302X</identifier>
    <identifier type="url">https://www.frontiersin.org/articles/10.3389/fmicb.2023.1250806/</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-18271</identifier>
    <enrichment key="opus.import.date">2023-11-23T09:28:17+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3389/fmicb.2023.1250806</enrichment>
    <enrichment key="SourceTitle">Marcos-Zambrano LJ, López-Molina VM, Bakir-Gungor B, Frohme M, Karaduzovic-Hadziabdic K, Klammsteiner T, Ibrahimi E, Lahti L, Loncar-Turukalo T, Dhamo X, Simeon A, Nechyporenko A, Pio G, Przymus P, Sampri A, Trajkovik V, Lacruz-Pleguezuelos B, Aasmets O, Araujo R, Anagnostopoulos I, Aydemir &amp;, Berland M, Calle ML, Ceci M, Duman H, Gündoğdu A, Havulinna AS, Kaka Bra KHN, Kalluci E, Karav S, Lode D, Lopes MB, May P, Nap B, Nedyalkova M, Paciência I, Pasic L, Pujolassos M, Shigdel R, Susín A, Thiele I, Truică C-O, Wilmes P, Yilmaz E, Yousef M, Claesson MJ, Truu J and Carrillo de Santa Pau E (2023) A toolbox of machine learning software to support microbiome analysis. Front. Microbiol. 14:1250806. doi: 10.3389/fmicb.2023.1250806</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Laura Judith Marcos-Zambrano</author>
    <author>Víctor Manuel López-Molina</author>
    <author>Burcu Bakir-Gungor</author>
    <author>Marcus Frohme</author>
    <author>Kanita Karaduzovic-Hadziabdic</author>
    <author>Thomas Klammsteiner</author>
    <author>Eliana Ibrahimi</author>
    <author>Leo Lahti</author>
    <author>Tatjana Loncar-Turukalo</author>
    <author>Xhilda Dhamo</author>
    <author>Andrea Simeon</author>
    <author>Alina Nechyporenko</author>
    <author>Gianvito Pio</author>
    <author>Piotr Przymus</author>
    <author>Alexia Sampri</author>
    <author>Vladimir Trajkovik</author>
    <author>Blanca Lacruz-Pleguezuelos</author>
    <author>Oliver Aasmets</author>
    <author>Ricardo Araujo</author>
    <author>Ioannis Anagnostopoulos</author>
    <author>Önder Aydemir</author>
    <author>Magali Berland</author>
    <author>M. Luz Calle</author>
    <author>Michelangelo Ceci</author>
    <author>Hatice Duman</author>
    <author>Aycan Gündoğdu</author>
    <author>Aki S. Havulinna</author>
    <author>Kardokh Hama Najib Kaka Bra</author>
    <author>Eglantina Kalluci</author>
    <author>Sercan Karav</author>
    <author>Daniel Lode</author>
    <author>Marta B. Lopes</author>
    <author>Patrick May</author>
    <author>Bram Nap</author>
    <author>Miroslava Nedyalkova</author>
    <author>Inês Paciência</author>
    <author>Lejla Pasic</author>
    <author>Meritxell Pujolassos</author>
    <author>Rajesh Shigdel</author>
    <author>Antonio Susín</author>
    <author>Ines Thiele</author>
    <author>Ciprian-Octavian Truică</author>
    <author>Paul Wilmes</author>
    <author>Ercument Yilmaz</author>
    <author>Malik Yousef</author>
    <author>Marcus Joakim Claesson</author>
    <author>Jaak Truu</author>
    <author>Enrique Carrillo de Santa Pau</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>microbiome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>feature generation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>feature analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data integration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>microbial gene prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>microbial metabolic modeling</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1827/fmicb-14-1250806.pdf</file>
  </doc>
</export-example>
