- recipe ms2deepscore
Deep learning similarity measure for comparing MS/MS spectra with respect to their chemical similarity
- Homepage:
- License:
APACHE / Apache-2.0
- Recipe:
- Links:
doi: 10.1186/s13321-021-00558-4, biotools: ms2deepscore
ms2deepscore provides a Siamese neural network that is trained to predict molecular structural similarities (Tanimoto scores) from pairs of mass spectrometry spectra.
- package ms2deepscore¶
- versions:
2.4.0-0,2.3.0-0,2.2.0-0,2.1.0-0,2.0.0-0,1.0.0-0,0.5.0-0,0.4.0-0,0.3.0.1-0- depends matchms:
>=0.18.0- depends matplotlib-base:
3.7.2- depends numba:
- depends numpy:
>=1.20.3- depends pandas:
- depends python:
>=3.9- depends pytorch:
- depends scikit-learn:
- depends tensorboard:
- depends torchvision:
- depends tqdm:
- 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 ms2deepscore and update with:: mamba update ms2deepscore
To create a new environment, run:
mamba create --name myenvname ms2deepscore
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/ms2deepscore:<tag> (see `ms2deepscore/tags`_ for valid values for ``<tag>``)
Download stats¶
Link to this page¶
Render an 
badge with the following MarkDown:
[](http://bioconda.github.io/recipes/ms2deepscore/README.html)
