AI Medical Compendium Journal:
The New phytologist

Showing 11 to 15 of 15 articles

Pollen analysis using multispectral imaging flow cytometry and deep learning.

The New phytologist
Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensi...

StomataCounter: a neural network for automatic stomata identification and counting.

The New phytologist
Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user-friendly, fully automated methods to perform the task of identifying and counting st...

Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale.

The New phytologist
Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery...

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.

The New phytologist
The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate betwee...

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

The New phytologist
Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pat...