Author

J. B. Nielsen

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 1 Ecology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:bacteria (other)· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:genetic perturbation / CRISPR· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

A. Perez· 1 shared A. Ramirez· 1 shared B. J. Sanchez· 1 shared C. M. Whitford· 1 shared E. Abeliuk· 1 shared E. E. Kontou· 1 shared F. Helsing-Nielsen· 1 shared H. G. Martin· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperBioengineering

A. Perez

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

MicrobiologybioRxivNov 6, 2020

Characterization and Engineering of Streptomyces griseofuscus DSM 40191 as a Potential Host for Heterologous Expression of Biosynthetic Gene Clusters

T. Gren, C. M. Whitford, O. S. Mohite…·T. Weber

This paper characterizes Streptomyces griseofuscus DSM 40191 as a potential heterologous host for biosynthetic gene clusters and demonstrates its ability to produce secondary metabolites.

DOI 10.1038/s41598-021-97571-2

bacteria (other)whole-genome sequencing

𝕏 PostBluesky

EcologybioRxivMar 18, 2020

An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning

K. Bjerge, J. B. Nielsen, M. V. Sepstrup…·T. T. Hoye

This paper introduces an automated moth trap with computer vision and deep learning to monitor moths, achieving high image capture and classification performance.

DOI 10.3390/s21020343

multiple speciesimaging

𝕏 PostBluesky

BioengineeringbioRxivPreprintNov 29, 2019

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

J. Zhang, S. D. Petersen, T. Radivojevic…·M. K. Jensen

This study combines mechanistic and machine learning models to optimize tryptophan metabolism in yeast, achieving a 106% increase in tryptophan accumulation through predictive engineering.

DOI 10.1101/858464

yeastmetabolomics

𝕏 PostBluesky