AIMC Topic: Arabidopsis

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Quantification of Mechanical Forces and Physiological Processes Involved in Pollen Tube Growth Using Microfluidics and Microrobotics.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2020
Pollen tubes face many obstacles on their way to the ovule. They have to decide whether to navigate around cells or penetrate the cell wall and grow through it or even within it. Besides chemical sensing, which directs the pollen tubes on their path ...
Robotics Calcium Lab-On-A-Chip Devices Arabidopsis Pollen Tube Microfluidics Stress, Mechanical
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A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth.

GigaScience May 1, 2019
BACKGROUND: Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases ...
Photometry Arabidopsis Phenotype Imaging, Three-Dimensional Deep Learning Plant Development
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Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Molecular omics Dec 3, 2018
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes t...
Metabolomics Cell Line Machine Learning Computational Biology Stress, Physiological Multivariate Analysis Mutation Sucrose Arabidopsis Phosphoglucomutase Analysis of Variance Chlorophyll
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Genome-wide pre-miRNA discovery from few labeled examples.

Bioinformatics (Oxford, England) Feb 15, 2018
MOTIVATION: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative ex...
Eukaryota Genome Arabidopsis Anopheles Nucleic Acid Conformation Caenorhabditis elegans MicroRNAs Supervised Machine Learning Genomics Computational Biology Sequence Analysis, RNA Sequence Analysis, DNA Animals
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agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update.

Nucleic acids research Jul 3, 2017
The agriGO platform, which has been serving the scientific community for >10 years, specifically focuses on gene ontology (GO) enrichment analyses of plant and agricultural species. We continuously maintain and update the databases and accommodate th...
Fishes Arabidopsis Software Agriculture Insecta Internet Animals Vegetables Molecular Sequence Annotation Birds Gene Ontology Crops, Agricultural
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TSSPlant: a new tool for prediction of plant Pol II promoters.

Nucleic acids research May 5, 2017
Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). M...
Promoter Regions, Genetic Neural Networks, Computer RNA Polymerase II Software Oryza Sequence Analysis, DNA Plant Proteins Gene Expression Genome, Plant Transcription Initiation Site Arabidopsis
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GENIUS: web server to predict local gene networks and key genes for biological functions.

Bioinformatics (Oxford, England) Mar 1, 2017
SUMMARY: GENIUS is a user-friendly web server that uses a novel machine learning algorithm to infer functional gene networks focused on specific genes and experimental conditions that are relevant to biological functions of interest. These functions ...
Gene Regulatory Networks Machine Learning Software Arabidopsis
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