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Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification
In: Frontiers in Marine Science, Jg. 72020
A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study
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Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform
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MAIA - A machine learning assisted image annotation method for environmental monitoring and exploration
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A Normalized Tree Index for identification of correlated clinical parameters in microarray data
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A Novel methodology for characterizing cell subpopulations in automated time-lapse microscopy
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On the impact of Citizen Science-derived data quality on deep learning based classification in marine images
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RecoMIA - Recommendations for marine image annotation: Lessons learned and future directions
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Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging
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