Metadata-Version: 2.1
Name: geometricus
Version: 0.3.0
Summary: Fast, structure-based, alignment-free protein embedding
Home-page: https://github.com/TurtleTools/geometricus
Author: Janani Durairaj, Mehmet Akdel
Author-email: janani.durairaj@unibas.ch
License: MIT License
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Description-Content-Type: text/markdown
License-File: LICENSE

<p align="center"><img src="geometricus_logo.png" width="300" title="Geometricus Logo"></p>

[![PyPI version](https://badge.fury.io/py/geometricus.svg)](https://badge.fury.io/py/geometricus)
[![DOI](https://zenodo.org/badge/doi/10.1093/bioinformatics/btaa839.svg)](http://dx.doi.org/10.1093/bioinformatics/btaa839)

# Geometricus Represents Protein Structures as Shape-mers derived from Moment Invariants

A structure-based, alignment-free embedding approach for proteins. Can be used as input to machine learning algorithms.

See the [documentation](https://turtletools.github.io/geometricus/).

## Installation
Geometricus is a Python (3.7+) package with NumPy, SciPy, Numba and ProDy as dependencies. 

Install with `pip install geometricus`

## Usage
See the [Getting Started](https://turtletools.github.io/geometricus/getting_started) page for example usage.

## Publications

Janani Durairaj, Mehmet Akdel, Dick de Ridder, Aalt D J van Dijk, Geometricus represents protein structures as shape-mers derived from moment invariants, 
Bioinformatics, Volume 36, Issue Supplement_2, December 2020, Pages i718–i725, https://doi.org/10.1093/bioinformatics/btaa839


Janani Durairaj, Mehmet Akdel, Dick de Ridder, Aalt D.J. van Dijk, Fast and adaptive protein structure representations for machine learning,
bioRxiv 2021.04.07.438777; doi: https://doi.org/10.1101/2021.04.07.438777


