Computational approaches to chemical hazard assessment

Lade...
Vorschaubild
Dateien
Luechtefeld_2--pe5kpp1b39us2.pdf
Luechtefeld_2--pe5kpp1b39us2.pdfGröße: 4.04 MBDownloads: 933
Datum
2017
Autor:innen
Luechtefeld, Thomas
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Link zur Lizenz
EU-Projektnummer
DFG-Projektnummer
Projekt
Open Access-Veröffentlichung
Sammlungen
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Alternatives to animal experimentation : ALTEX. 2017, 34(4), pp. 459-478. ISSN 1868-596X. eISSN 1868-8551. Available under: doi: 10.14573/altex.1710141
Zusammenfassung

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
570 Biowissenschaften, Biologie
Schlagwörter
QSAR, machine learning, cheminformatics, molecular descriptor, toxicology
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690LUECHTEFELD, Thomas, Thomas HARTUNG, 2017. Computational approaches to chemical hazard assessment. In: Alternatives to animal experimentation : ALTEX. 2017, 34(4), pp. 459-478. ISSN 1868-596X. eISSN 1868-8551. Available under: doi: 10.14573/altex.1710141
BibTex
@article{Luechtefeld2017Compu-40824,
  year={2017},
  doi={10.14573/altex.1710141},
  title={Computational approaches to chemical hazard assessment},
  number={4},
  volume={34},
  issn={1868-596X},
  journal={Alternatives to animal experimentation : ALTEX},
  pages={459--478},
  author={Luechtefeld, Thomas and Hartung, Thomas}
}
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/123456789/40824">
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:creator>Hartung, Thomas</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/40824/1/Luechtefeld_2--pe5kpp1b39us2.pdf"/>
    <dc:language>eng</dc:language>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dcterms:issued>2017</dcterms:issued>
    <dc:creator>Luechtefeld, Thomas</dc:creator>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/40824/1/Luechtefeld_2--pe5kpp1b39us2.pdf"/>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-12-04T10:54:27Z</dcterms:available>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-12-04T10:54:27Z</dc:date>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/40824"/>
    <dc:contributor>Hartung, Thomas</dc:contributor>
    <dcterms:title>Computational approaches to chemical hazard assessment</dcterms:title>
    <dc:contributor>Luechtefeld, Thomas</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:abstract xml:lang="eng">Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.</dcterms:abstract>
  </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
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet