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

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

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ALTANER, 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. 37(1). ISSN 1868-596X. eISSN 1868-8551. Available under: doi: 10.14573/altex.1904031

@article{Altaner2020Machi-46476, title={Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans}, year={2020}, doi={10.14573/altex.1904031}, 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 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/rdf/resource/123456789/46476"> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Jaeger, Sabrina</dc:creator> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dc:rights>Attribution 4.0 International</dc:rights> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/46476"/> <dc:contributor>Zemskov, Ivan</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> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/46476/1/Altaner_2-tbijoto6dpml4.pdf"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/28"/> <dc:creator>Dietrich, Daniel R.</dc:creator> <dc:contributor>Jaeger, Sabrina</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/28"/> <dc:contributor>Fotler, Regina</dc:contributor> <dcterms:issued>2020</dcterms:issued> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/29"/> <dcterms:title>Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans</dcterms:title> <dc:creator>Zemskov, Ivan</dc:creator> <dc:contributor>Dietrich, Daniel R.</dc:contributor> <dc:creator>Fotler, Regina</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-07-22T13:19:58Z</dc:date> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/46476/1/Altaner_2-tbijoto6dpml4.pdf"/> <dc:creator>Wittmann, Valentin</dc:creator> <dc:creator>Altaner, Stefan</dc:creator> <dc:contributor>Wittmann, Valentin</dc:contributor> <dc:contributor>Altaner, Stefan</dc:contributor> <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> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/29"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:contributor>Schreiber, Falk</dc:contributor> </rdf:Description> </rdf:RDF>

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