Publikation:

Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans

Lade...
Vorschaubild

Dateien

Altaner_2-tbijoto6dpml4.pdf
Altaner_2-tbijoto6dpml4.pdfGröße: 2.65 MBDownloads: 448

Datum

2020

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Link zur Lizenz

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Gold
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Alternatives to Animal Experimentation : ALTEX. ALTEX Edition. 2020, 37(1). ISSN 1868-596X. eISSN 1868-8551. Available under: doi: 10.14573/altex.1904031

Zusammenfassung

Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of one MC-congener (MC-LR) only. MC congener structural diversity is a challenge for the risk assessment of these toxins, especially as several different PPPs have to be included in the RA. Consequently, the inhibition of PPP1, PPP2A and PPP5 was determined with 18 structurally different MC and demonstrated MC congener dependent inhibition activity and a lower susceptibility of PPP5 to inhibition than PPP1 and PPP2A. The latter data were employed to train a machine learning algorithm that should allow prediction of PPP inhibition (toxicity) based on MCs 2D chemical structure. IC50 values were classified in toxicity classes and three machine learning models were used to predict the toxicity class, resulting in 80-90% correct predictions.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
570 Biowissenschaften, Biologie

Schlagwörter

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Verknüpfte Datensätze

Zitieren

ISO 690ALTANER, Stefan, Sabrina JAEGER, Regina FOTLER, Ivan ZEMSKOV, Valentin WITTMANN, Falk SCHREIBER, Daniel R. DIETRICH, 2020. Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans. In: Alternatives to Animal Experimentation : ALTEX. ALTEX Edition. 2020, 37(1). ISSN 1868-596X. eISSN 1868-8551. Available under: doi: 10.14573/altex.1904031
BibTex
@article{Altaner2020Machi-46476,
  year={2020},
  doi={10.14573/altex.1904031},
  title={Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans},
  number={1},
  volume={37},
  issn={1868-596X},
  journal={Alternatives to Animal Experimentation : ALTEX},
  author={Altaner, Stefan and Jaeger, Sabrina and Fotler, Regina and Zemskov, Ivan and Wittmann, Valentin and Schreiber, Falk and Dietrich, Daniel R.},
  note={Erratum: https://doi.org/10.14573/altex.1904031e}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46476">
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-07-22T13:19:58Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/29"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:creator>Jaeger, Sabrina</dc:creator>
    <dc:contributor>Zemskov, Ivan</dc:contributor>
    <dc:language>eng</dc:language>
    <dc:creator>Altaner, Stefan</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Zemskov, Ivan</dc:creator>
    <dc:contributor>Altaner, Stefan</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/46476"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/46476/1/Altaner_2-tbijoto6dpml4.pdf"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dc:contributor>Fotler, Regina</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:contributor>Schreiber, Falk</dc:contributor>
    <dc:contributor>Jaeger, Sabrina</dc:contributor>
    <dcterms:title>Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans</dcterms:title>
    <dc:creator>Fotler, Regina</dc:creator>
    <dc:contributor>Dietrich, Daniel R.</dc:contributor>
    <dc:creator>Schreiber, Falk</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-07-22T13:19:58Z</dcterms:available>
    <dc:creator>Dietrich, Daniel R.</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:abstract xml:lang="eng">Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of one MC-congener (MC-LR) only. MC congener structural diversity is a challenge for the risk assessment of these toxins, especially as several different PPPs have to be included in the RA. Consequently, the inhibition of PPP1, PPP2A and PPP5 was determined with 18 structurally different MC and demonstrated MC congener dependent inhibition activity and a lower susceptibility of PPP5 to inhibition than PPP1 and PPP2A. The latter data were employed to train a machine learning algorithm that should allow prediction of PPP inhibition (toxicity) based on MCs 2D chemical structure. IC50 values were classified in toxicity classes and three machine learning models were used to predict the toxicity class, resulting in 80-90% correct predictions.</dcterms:abstract>
    <dc:creator>Wittmann, Valentin</dc:creator>
    <dc:contributor>Wittmann, Valentin</dc:contributor>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/29"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/46476/1/Altaner_2-tbijoto6dpml4.pdf"/>
    <dcterms:issued>2020</dcterms:issued>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Erratum: https://doi.org/10.14573/altex.1904031e
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Diese Publikation teilen