Bricks
gws_gaia
GAIA (Gencovery Artificial Intelligence Analytics) provides core features for machine learning and deep learning modelling. It offers a broad range of AI algorithms, covering among others classification, regression, clustering methods, neural networks.
gws_ode
Brick for Ordinary Differential Equations
gws_stats
STATS (Statistical Tools Suite) provides core features for statistical analysis of biological data. It offers the most widely used statistical methods for biological data analysis to quantitatively assess your hypothesis from your data.
gws_scomix
This brick allows users to analyse scRNAseq datasets
gws_gena
GENA provides core features to create and use actionable digital twins of cell metabolism to understand and predict cell mechanisms of action.
gws_ubiome
uBiome provides core features for 16S rRNA short-read sequencing analysis
gws_sim
Gencovery brick for dynamical system simulations
gws_core
Core brick for Constellab. This brick is included in any lab and manage the core functionalities of the lab (experiments, resource, reports...). It contains generic tasks and resources for your pipeline.
gws_biota
BIOTA is a unified and structured collection of omics data collected from official open European (EMBL-EBI) and NCBI taxonomy knowledge bases. More than 2 M organisms are referenced in BIOTA with their metabolic characteristics.
gws_metag
Short-reads and long-reads metagenomic assembly and annotation pipeline