Derya Aytan-Aktug

Technical University of Denmark

Denmark

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Professors in Microbiology

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Articles (10)

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Collaborators (12)

Line Clemmensen

Technical University of Denmark

DENMARK

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Philip Clausen

Technical University of Denmark

DENMARK

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Judit Szarvas

Københavns Universitet

DENMARK

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Thomas Nordahl Petersen

Technical University of Denmark

DENMARK

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Andrew Warren

Research Assistant Professor

University of Virginia

UNITED STATES

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Ole Lund

Technical University of Denmark

DENMARK

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Sara Ludovico Garcia

Københavns Universitet

DENMARK

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Rikke Linnemann Nielsen

DENMARK

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Rolf Sommer Kaas

Technical University of Denmark

DENMARK

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Patrick Munk

Technical University of Denmark

DENMARK

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Marianne Helenius

Technical University of Denmark

DENMARK

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Victoria Janes

University of Amsterdam

NETHERLANDS

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Derya Aytan-Aktug

Research Interests

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Contact this professor

Articles (10)

SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest

ResFinder – an open online resource for identification of antimicrobial resistance genes in next-generation sequencing data and prediction of phenotypes from genotypes

Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Can Machine Learning Models Predict Asparaginase-associated Pancreatitis in Childhood Acute Lymphoblastic Leukemia

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing

Data integration for prediction of weight loss in randomized controlled dietary trials.

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Collaborators (12)

Line Clemmensen

Philip Clausen

Judit Szarvas

Thomas Nordahl Petersen

Andrew Warren

Ole Lund

Sara Ludovico Garcia

Rikke Linnemann Nielsen

Rolf Sommer Kaas

Patrick Munk

Marianne Helenius

Victoria Janes

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