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Literatur
Peak intensity prediction in mass spectra using machine learning methods
Timm, Wiebke
2008
ALMI - A Generic Active Learning System for Computational Object Classification in Marine Observation Images
Möller, Torben
;
Nattkemper, Tim Wilhelm
In: Sensors, Vol. 21 Issue 4
Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification
Langenkämper, Daniel
;
van Kevelaer, Robin
;
Purser, Autun
;
Nattkemper, Tim Wilhelm
In: Frontiers in Marine Science, Vol. 7
MAIA - A machine learning assisted image annotation method for environmental monitoring and exploration
Zurowietz, Martin
;
Langenkämper, Daniel
;
Hosking, Brett
;
Ruhl, Henry
;
Nattkemper, Tim Wilhelm
In: PLoS ONE, Vol. 13 Issue 11
Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics
Timm, Wiebke
;
Scherbart, Alexandra
;
Boecker, Sebastian
;
Kohlbacher, Oliver
;
Nattkemper, Tim Wilhelm
In: BMC Bioinformatics, Vol. 9 Issue 1, page 443 et seq.
Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform
Kessler, Nikolas
;
Bonte, Anja
;
Albaum, Stefan
;
Mäder, Paul
;
Messmer, Monika
;
Goesmann, Alexander
;
Niehaus, Karsten
;
Langenkämper, Georg
;
Nattkemper, Tim Wilhelm
In: Frontiers in Bioinformatics and Computational Biology, Vol. 3
On the impact of Citizen Science-derived data quality on deep learning based classification in marine images
Langenkämper, Daniel
;
Simon-Lledó, Erik
;
Hosking, Brett
;
Jones, Daniel O. B.
;
Nattkemper, Tim Wilhelm
In: PLOS ONE, Vol. 14 Issue 6