AIMC Topic: Arabidopsis

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Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Molecular omics
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...

Genome-wide pre-miRNA discovery from few labeled examples.

Bioinformatics (Oxford, England)
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...

agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update.

Nucleic acids research
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...

TSSPlant: a new tool for prediction of plant Pol II promoters.

Nucleic acids research
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...

GENIUS: web server to predict local gene networks and key genes for biological functions.

Bioinformatics (Oxford, England)
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 ...