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Fast-tracking the identification of safe and effective probiotic bacteria by in sillico prediction of bacterial genomic features
Tang Karlsen, Signe
(PhD Student)
Bælum, Jacob
(Supervisor)
Vesth, Tammi Camilla
(Supervisor)
Petersen, Thomas Nordahl
(Examiner)
Lagesen, Karin
(Examiner)
Tolstrup, Niels
(Examiner)
Lund, Ole
(Main Supervisor)
National Food Institute
Overview
Fingerprint
Publications
(1)
Project Details
Status
Finished
Effective start/end date
01/08/2017
→
20/05/2021
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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Phenotype
Agricultural and Biological Sciences
100%
Bacteria
Agricultural and Biological Sciences
83%
Bacterium
Medicine and Dentistry
83%
Plant Health
Agricultural and Biological Sciences
16%
Enzymes
Agricultural and Biological Sciences
16%
Animal Health
Agricultural and Biological Sciences
16%
Fermented Food
Agricultural and Biological Sciences
16%
Bacilli
Agricultural and Biological Sciences
16%
Research output
Publications per year
2021
2021
2021
1
Ph.D. thesis
Publications per year
Publications per year
Whole-genome based bacterial phenotype predictions with machine learning.
Tang Karlsen, S.
,
2021
,
Technical University of Denmark
.
166 p.
Research output
:
Book/Report
›
Ph.D. thesis
Open Access
File
Phenotype
100%
Bacteria
83%
Bacterium
83%
Hypothesis-Driven Approach
25%
Genomics
16%
229
Downloads (Pure)