%0 Book
%T Machine learning classification of microbial community compositions to predict anthropogenic pollutants in the Baltic Sea
%A Janßen, René
%D 2020
%C Rostock
%C Universität Rostock
%G English
%F 1744741417
%O vorgelegt von René Janßen
%O Enthält Zeitschriftenartikel
%O GutachterInnen: Matthias Labrenz (Leibniz-Institut für Ostseeforschung Warnemünde) ; Rudolf Amann (Max-Planck-Institut für Marine Mikrobiologie) ; Alexander Probst (Universität Duisburg-Essen) ; Stephen Techtmann (Michigan Technological University)
%O Dissertation Universität Rostock 2020 Kumulative Dissertation
%X Microbial communities react rapidly and specifically to changing environments, indicating distinct microbial fingerprints for a given environmental state. Machine learning with community data predicted the Baltic Sea-detected pollutants glyphosate and 2,4,6-trinitrotoluene, using the developed R package “phyloseq2ML”. Predictions by Random Forest and Artificial Neural Network were accurate. Relevant taxa were identified. The interpretability of machine learning models was found of particular importance. Microbial communities predicted even minor influencing factors in complex environments.<eng>
%L 004
%9 theses
%9 Text
%9 Hochschulschrift
%R 10.18453/rosdok_id00002897
%U http://purl.uni-rostock.de/rosdok/id00002897
%U https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00002897-8
%U https://d-nb.info/1293662267/34
%U https://doi.org/10.18453/rosdok_id00002897