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Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.
In Biosensors
Zubler Alanna V, Yoon Jeong-Yeol
2020-Nov-29
RGB imaging, abiotic stress, artificial neural network (ANN), fluorescence, hyperspectral imaging, machine learning, plant disease, smartphone imaging, support vector machine (SVM), thermography

Interpretability and Explainability: A Machine Learning Zoo Mini-tour
ArXiv Preprint
Ričards Marcinkevičs, Julia E. Vogt
2020-12-03

Tackling the challenges of bioimage analysis.
In eLife
Pelt Daniël M
2020-Dec-02
bioimage informatics, computational biology, deep learning, fluorescence microscopy, mouse, neuroscience, objectivity, reproducibility, systems biology, validity, zebrafish

Practical Workflow from High-Throughput Genotyping to Genomic Estimated Breeding Values (GEBVs).
In Methods in molecular biology (Clifton, N.J.)
Contaldi Felice, Cappetta Elisa, Esposito Salvatore
2021
Genomic estimated breeding values (GEBVs), Machine learning, Next-generation breeding, Single-nucleotide polymorphisms (SNPs), Stacks, rrBLUP

[Technical, operational, and regulatory considerations for the adoption of digital and computational pathology].
In Der Pathologe
BACKGROUND :
OBJECTIVES :
MATERIAL AND METHODS :
RESULTS :
DISCUSSION :
Herrmann Markus D, Lennerz Jochen K
2020-Dec-02
Artificial intelligence, In vitro diagnostics, Machine learning, Quality assurance, Regulatory science

Borderline personality disorder classification based on brain network measures during emotion regulation.
In European archives of psychiatry and clinical neuroscience
Cremers Henk, van Zutphen Linda, Duken Sascha, Domes Gregor, Sprenger Andreas, Waldorp Lourens, Arntz Arnoud
2020-Dec-02
Borderline personality disorder, Classification, Machine learning, Network measures, Networks analysis, Phasic vs. tonic brain connectivity
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