@Book{1744741417,
author="Jan{\ss}en, Ren{\'e}",
title="Machine learning classification of microbial community compositions to predict anthropogenic pollutants in the Baltic Sea",
year="2020",
address="Rostock",
abstract="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>",
school="Universit{\"a}t Rostock",
note="vorgelegt von Ren{\'e} Jan{\ss}en",
note="Enth{\"a}lt Zeitschriftenartikel",
note="GutachterInnen: Matthias Labrenz (Leibniz-Institut f{\"u}r Ostseeforschung Warnem{\"u}nde) ; Rudolf Amann (Max-Planck-Institut f{\"u}r Marine Mikrobiologie) ; Alexander Probst (Universit{\"a}t Duisburg-Essen) ; Stephen Techtmann (Michigan Technological University)",
note="Dissertation Universit{\"a}t Rostock 2020 Kumulative Dissertation",
doi="10.18453/rosdok_id00002897",
url="http://purl.uni-rostock.de/rosdok/id00002897",
url="https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00002897-8",
url="https://d-nb.info/1293662267/34",
url="https://doi.org/10.18453/rosdok_id00002897",
language="English"
}