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Poulsen, Vera Kuzina
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1

Composition of bacterial blends for fermentation-induced pe..:

Masiá, Carmen ; Fernández-Varela, Raquel ; Poulsen, Vera Kuzina..
Food Bioscience.  56 (2023)  - p. 103333 , 2023
Link: https://doi.org/10.1016/..
 
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2

Versatile Lactococcus lactis strains improve texture in bot..:

Poulsen, Vera Kuzina ; Moghadam, Elahe Ghanei ; Kračun, Stjepan Krešimir...
FEMS Microbiology Letters.  369 (2022)  1 - p. , 2022
Link: https://doi.org/10.1093/..
 
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3

Measurement of water-holding capacity in fermented milk usi..:

Havmand, Pernille Uma ; Zachariassen, Lau Gustav ; Ipsen, Richard.
Journal of Dairy Research.  89 (2022)  2 - p. 194-200 , 2022
Link: https://doi.org/10.1017/..
 
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4

Machine learning predicts and provides insights into milk a..:

Karlsen, Signe Tang ; Vesth, Tammi Camilla ; Oregaard, Gunnar...
PLOS ONE.  16 (2021)  3 - p. e0246287 , 2021
Link: https://doi.org/10.1371/..
 
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5

Screening for texturing Leuconostoc and genomics behind pol..:

Poulsen, Vera Kuzina ; Koza, Anna ; Al-Nakeeb, Kosai.
FEMS Microbiology Letters.  367 (2020)  20 - p. , 2020
Link: https://doi.org/10.1093/..
 
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6

High-throughput screening for texturingLactococcusstrains:

Poulsen, Vera Kuzina ; Derkx, Patrick ; Oregaard, Gunnar
FEMS Microbiology Letters.  366 (2019)  2 - p. , 2019
Link: https://doi.org/10.1093/..
 
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7

Polysaccharide production by lactic acid bacteria: from gen..:

Zeidan, Ahmad A. ; Poulsen, Vera Kuzina ; Janzen, Thomas...
FEMS Microbiology Reviews.  41 (2017)  Supp_1 - p. S168-S200 , 2017
Link: https://doi.org/10.1093/..
 
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8

Machine learning predicts and provides insights into milk a..:

Karlsen, Signe Tang ; Vesth, Tammi Camilla ; Oregaard, Gunnar...
https://orbit.dtu.dk/en/publications/605d02a1-fc92-431f-bb23-c14829592569.  , 2021
Link: https://orbit.dtu.dk/en/..
 
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9

Generation of appropriate learning activities for a student..:

Kuzina Poulsen, Vera
https://tidsskrift.dk/IUSTL/article/view/140058/184062.  , 2023
Link: https://tidsskrift.dk/IU..
 
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10

Machine learning predicts and provides insights into milk a..:

Signe Tang Karlsen ; Tammi Camilla Vesth ; Gunnar Oregaard...
https://doi.org/10.1371/journal.pone.0246287.  , 2021
Link: https://doi.org/10.1371/..
 
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11

Phenotype matrix:

Signe Tang Karlsen (10305303) ; Tammi Camilla Vesth (10305306) ; Gunnar Oregaard (10220702)...
https://figshare.com/articles/dataset/Phenotype_matrix_/14217741.  , 2021
Link: https://doi.org/10.1371/..
 
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12

Distribution of V max with regard to phylogeny:

Signe Tang Karlsen (10305303) ; Tammi Camilla Vesth (10305306) ; Gunnar Oregaard (10220702)...
https://figshare.com/articles/figure/Distribution_of_V_sub_max_sub_with_regard_to_phylogeny_/14217747.  , 2021
Link: https://doi.org/10.1371/..
 
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13

Pangenome gene distribution:

Signe Tang Karlsen (10305303) ; Tammi Camilla Vesth (10305306) ; Gunnar Oregaard (10220702)...
https://figshare.com/articles/dataset/Pangenome_gene_distribution_/14217765.  , 2021
Link: https://doi.org/10.1371/..
 
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14

Gene matrix:

Signe Tang Karlsen (10305303) ; Tammi Camilla Vesth (10305306) ; Gunnar Oregaard (10220702)...
https://figshare.com/articles/dataset/Gene_matrix_/14217726.  , 2021
Link: https://doi.org/10.1371/..
 
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15

The 9-mers with the highest feature importances and the gen..:

Signe Tang Karlsen (10305303) ; Tammi Camilla Vesth (10305306) ; Gunnar Oregaard (10220702)...
https://figshare.com/articles/journal_contribution/The_9-mers_with_the_highest_feature_importances_and_the_genes_in_which_they_occur_/14217714.  , 2021
Link: https://doi.org/10.1371/..
 
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