Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10638
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Main Title: Supporting Information and Data for Machine Learning Algorithms Applied to Identify Microbial Species by their Motility
Author(s): Riekeles, Max
Type: Generic Research Data
Language Code: en
Abstract: Future missions to potentially habitable places in the Solar System require biochemistry-independent methods for detecting potentially alien life forms, for which ‘motility’ is an excellent candidate. Here we present a semi-automated experimental setup, capable of distinguishing the movement of abiotic particles due to Brownian motion from the motility behavior of the bacteria Pseudoalteromonas haloplanktis, Planococcus halocryophilus, Bacillus subtilis, and Escherichia coli. Supervised machine learning algorithms were also used to specifically identify these species based on their characteristic motility behavior. While we were able to distinguish microbial motility from the abiotic movements due to Brownian motion with an accuracy exceeding 99%, the accuracy of the automated identification rates of the selected species does not exceed 82%. This study serves as the basis for the further development of a microscopic life recognition system for upcoming missions to Mars or the ocean worlds of the outer Solar System.
URI: https://depositonce.tu-berlin.de/handle/11303/11750
http://dx.doi.org/10.14279/depositonce-10638
Issue Date: 2020
Date Available: 13-Oct-2020
DDC Class: 500 Naturwissenschaften und Mathematik
005 Computerprogrammierung, Programme, Daten
Subject(s): machine learning algorithms
microbial motility
motility
microorganisms
microscopic life
recognition system
algorithm
Brownian motion
License: https://creativecommons.org/licenses/by/4.0/
Appears in Collections:Zentrum für Astronomie und Astrophysik » Research Data

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