ratvec 0.1.1

Generating dense embeddings for proteins using kernel PCA

This tool generates low-dimensional, continuous, distributed vector representations for non-numeric entities such as text or biological sequences (e.g. DNA or proteins) via kernel PCA with rational kernels.

The current implementation accepts any input dataset that can be read as a list of strings.

Installation

Install directly from the source with:

$ pip install git+https://github.com/ratvec/ratvec.git

Install in development mode with:

$ git clone https://github.com/ratvec/ratvec.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 automatically installs a command line interface. Check it out with:

$ ratvec --help

RatVec has three main commands: generate, train, and evaluate:

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

$ ratvec generate

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

$ ratvec train --help

3. Evaluate. Evaluate and optimize KPCA embeddings. Please run the following command to see the arguments:

$ ratvec evaluate --help

Showcase Dataset

The application presented in the paper (SwissProt dataset [1] used by Boutet et al. [2]) can be downloaded directly from here or running the following command:

$ ratvec generate

References

[1]

Boutet, E. et al. (2016). UniProtKB/Swiss-Prot, the manually annotated section of the UniProt KnowledgeBase: how to use the entry view.. Plant Bioinformatics (pp. 23-54).

[2]

Asgari, E., & Mofrad, M. R. (2015). Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one, 10(11), e0141287.