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Generating dense embeddings for proteins using kernel PCA

Project description

Generating dense embeddings for proteins using kernel PCA.

Installation

Install directly from the source with:

$ pip install git+https://jira.iais.fraunhofer.de/stash/scm/meml/protein_vectors.git

Install in development mode with:

$ git clone https://jira.iais.fraunhofer.de/stash/scm/meml/protein_vectors.git
$ cd ratvec
$ pip install -e .

The -e dynamically links the code in the git repository to the Python site-packages so your changes get reflected immediately.

How to Use

ratvec is automatically installs a command line interface. Check it out with

$ ratvec --help

RatVec has four main commands: generate, train, evaluate and optimize:

  1. Generate. Downloads and prepare the SwissProt data set that is showcased in the RatVec paper.

  2. Train. Compute KPCA embeddings on a given data set. Please run the following command to see the arguments:

$ ratvec train --help
  1. Evaluate. Evaluate and optimize KPCA embeddings. Please run the following command to see the arguments:

$ ratvec evaluate --help
  1. Optimize. Evaluate and optimize KPCA embeddings. Please run the following command to see the arguments:

$ ratvec optimize --help

Showcase Dataset

The application presented in the paper (SwissProt dataset [1] used by Boutet et al. [2]) can be downloaded directly from the following website https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JMFHTN or by running the following command:

$ ratvec generate

References

Project details


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