TY  - JOUR
A1  - Mayer, Benjamin
A1  - Holtrup, Sven
A1  - Graumann, Peter
T1  - A machine learning-empowered workflow to discriminate bacillus subtilis motility phenotypes
T2  - BioMedInformatics
N2  - Bacteria that are capable of organizing themselves as biofilms are an important public health issue. Knowledge discovery focusing on the ability to swarm and conquer the surroundings to form persistent colonies is therefore very important for microbiological research communities that focus on a clinical perspective. Here, we demonstrate how a machine learning workflow can be used to create useful models that are capable of discriminating distinct associated growth behaviors along distinct phenotypes. Based on basic gray-scale images, we provide a processing pipeline for binary image generation, making the workflow accessible for imaging data from a wide range of devices and conditions. The workflow includes a locally estimated regression model that easily applies to growth-related data and a shape analysis using identified principal components. Finally, we apply a density-based clustering application with noise (DBSCAN) to extract and analyze characteristic, general features explained by colony shapes and areas to discriminate distinct Bacillus subtilis phenotypes. Our results suggest that the differences regarding their ability to swarm and subsequently conquer the medium that surrounds them result in characteristic features. The differences along the time scales of the distinct latency for the colony formation give insights into the ability to invade the surroundings and therefore could serve as a useful monitoring tool.
KW  - microbiology
KW  - machine learning
KW  - motility
KW  - pathogenicity
KW  - swarming
KW  - biofilm
KW  - monitoring
KW  - shape
KW  - Bacillus subtilis
KW  - colony formation
KW  - workflow development
KW  - bioimaging
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/75590
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-755900
SN  - 2673-7426
VL  - 2
IS  - 4
SP  - 565
EP  - 579
PB  - MDPI
CY  - Basel
ER  -