- recipe flexynesis
A deep-learning-based multi-omics bulk sequencing data integration suite with a focus on (pre-)clinical endpoint prediction.
- Homepage:
- License:
OTHER
- Recipe:
This is a deep-learning based multi-omics bulk sequencing data integration suite with a focus on (pre-)clinical endpoint prediction.
- package flexynesis¶
- versions:
0.2.10-0- depends captum:
- depends ipykernel:
- depends ipywidgets:
- depends lifelines:
- depends lightning:
- depends matplotlib-base:
- depends numpy:
- depends pandas:
- depends papermill:
- depends python:
>=3.11,<3.12- depends python-louvain:
- depends pytorch:
- depends pytorch_geometric:
- depends pyyaml:
- depends rich:
- depends scikit-optimize:
- depends scikit-survival:
- depends scipy:
- depends seaborn:
- depends torchvision:
- depends tqdm:
- depends umap-learn:
- requirements:
Installation
You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).
While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.
Given that you already have a conda environment in which you want to have this package, install with:
mamba install flexynesis and update with:: mamba update flexynesis
To create a new environment, run:
mamba create --name myenvname flexynesis
with
myenvnamebeing a reasonable name for the environment (see e.g. the mamba docs for details and further options).Alternatively, use the docker container:
docker pull quay.io/biocontainers/flexynesis:<tag> (see `flexynesis/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/flexynesis/README.html)
