Fast, structure-based, alignment-free protein embedding
Project description
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.
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 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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file geometricus-0.3.0.tar.gz.
File metadata
- Download URL: geometricus-0.3.0.tar.gz
- Upload date:
- Size: 16.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.0 CPython/3.9.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
dbc87bbc07d49464058b07ad979644fe5752439549ebf3d41dc1d94bcbe14c35
|
|
| MD5 |
69f50a2d79b83b4e10c39ef37c46e90d
|
|
| BLAKE2b-256 |
62faa7e71461bf7ecbcee27fdad07e8a62eaa719029e5548f5e3582695d1d066
|
File details
Details for the file geometricus-0.3.0-py3-none-any.whl.
File metadata
- Download URL: geometricus-0.3.0-py3-none-any.whl
- Upload date:
- Size: 16.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.0 CPython/3.9.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3ddfba39bf47065743358d77a88a0d2f6c34deac607e8fd018f617a096d24d7f
|
|
| MD5 |
f09ad7e4ab4b48f029d08984460aa328
|
|
| BLAKE2b-256 |
8067d5745bce4e6646fc5688cba09be46a760d74b76ddd05fd738d53236df69e
|
