AIMC Topic: Plants

Clear Filters Showing 171 to 180 of 202 articles

Integration of machine learning with computational structural biology of plants.

The Biochemical journal
Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements...

Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases.

Methods in molecular biology (Clifton, N.J.)
Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions....

Machine learning for phytopathology: from the molecular scale towards the network scale.

Briefings in bioinformatics
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within...

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

Briefings in bioinformatics
This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusabl...

Accurate prediction of multi-label protein subcellular localization through multi-view feature learning with RBRL classifier.

Briefings in bioinformatics
Multi-label proteins can participate in carrier transportation, enzyme catalysis, hormone regulation and other life activities. Meanwhile, they play a key role in the fields of biopharmaceuticals, gene and cell therapy. This article proposes a predic...

DeepLearnMOR: a deep-learning framework for fluorescence image-based classification of organelle morphology.

Plant physiology
The proper biogenesis, morphogenesis, and dynamics of subcellular organelles are essential to their metabolic functions. Conventional techniques for identifying, classifying, and quantifying abnormalities in organelle morphology are largely manual an...

ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.

GigaScience
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence ...

Recent developments and potential of robotics in plant eco-phenotyping.

Emerging topics in life sciences
Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for...

High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales.

Emerging topics in life sciences
Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, mode...

Brief Survey on Machine Learning in Epistasis.

Methods in molecular biology (Clifton, N.J.)
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...