| Name | pcser JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | protein corona stealth effect prediction |
| upload_time | 2024-07-31 20:15:51 |
| maintainer | None |
| docs_url | None |
| author | Jianfeng Sun |
| requires_python | <4.0,>=3.11 |
| license | GPL-3.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="left">
<img src="https://github.com/2003100127/pcser/blob/main/img/pcser-logo.png?raw=true" width="200" height="70">
<br>
</h1>
[](https://pepy.tech/project/pcser)
<hr>
###### tags: `protein corona` `nanoparticles` `stealth effect` `machine learning`
## Overview
PCSER is a computational tool for predicting protein corona stealth effects. It was built using the random forest machine learning approach.
## 📔 Documentation
Please check https://2003100127.github.io/pcser for how to use PCSER.
## 🛠️ Installation
PCSER can be installed in the following ways.
*  (https://pypi.org/project/pcser)
```bash
conda create --name pcser python=3.11
conda activate pcser
pip install pcser --upgrade
```
* 
```bash
conda create --name pcser python=3.11
conda activate pcser
git clone https://github.com/2003100127/pcser.git
cd pcser
pip install .
```
## 🚀 Quick start
```python
import pcser as pcs
pcs.load.evaluate(
data_ref_fpn='./Proteomics_07262023_rv_C57BL6_spl54.xlsx',
sv_fp='./', # None to('data/')
input_fpn='./example.xlsx',
model_fpn='./best_cv.joblib',
sheet_name='a', # a b
# mfi_ref=[10271.33333, 10747, 10303.33333, 9663.333333, 10056],
mfi_ref=[3606.333333, 3606.333333, 3606.333333, 3606.333333],
# is_norm=True,
# norm_met='minmax', # minmax std maxabs
# mode='compo', # compo annot
# mark='spl54', # spl54 spl63
# version='extended', # extended old
)
```
Then, it outputs what is shown below.
```python
# You are using extended sheets.
# You have selected the minmax normalization method.
# Data summary:
# Number of samples: 54
# Number of features: 419
# You have the samples: ['HuApoA1', 'MoApoA1', 'HuClusterin', 'MoClusterin']
# PCSER predictions:
# stealth_effect MFI
# HuApoA1 0.670762 3099.790003
# MoApoA1 0.662108 3189.458730
# HuClusterin 0.634621 3474.270396
# MoClusterin 0.633914 3481.599008
# stealth_effect MFI
# HuApoA1 0.670762 3099.790003
# MoApoA1 0.662108 3189.458730
# HuClusterin 0.634621 3474.270396
# MoClusterin 0.633914 3481.599008
```
## 📄 Citation
```angular2html
@article{PCSER,
title = {PCSER},
author = {Jianfeng Sun},
doi = {xxx},
url = {https://github.com/2003100127/pcser},
journal = {xxx}
year = {2024},
}
```
## 🏠 Homepage
[📍Oxford University](https://www.ndorms.ox.ac.uk/team/jianfeng-sun)
## 📧 Reach us
[](https://www.linkedin.com/in/jianfeng-sun-2ba9b1132)
[](mailto:jianfeng.sunmt@gmail.com)
[](mailto:jianfeng.sun@ndorms.ox.ac.uk)
<a href="https://twitter.com/Jianfeng_Sunny" ><img src="https://img.shields.io/twitter/follow/Jianfeng_Sunny.svg?style=social" /> </a>
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"description": "<h1 align=\"left\">\n <img src=\"https://github.com/2003100127/pcser/blob/main/img/pcser-logo.png?raw=true\" width=\"200\" height=\"70\">\n <br>\n</h1>\n\n\n\n\n[](https://pepy.tech/project/pcser)\n\n<hr>\n\n\n\n\n\n###### tags: `protein corona` `nanoparticles` `stealth effect` `machine learning`\n\n## Overview\nPCSER is a computational tool for predicting protein corona stealth effects. It was built using the random forest machine learning approach.\n\n## \ud83d\udcd4 Documentation\nPlease check https://2003100127.github.io/pcser for how to use PCSER.\n\n## \ud83d\udee0\ufe0f Installation\n\nPCSER can be installed in the following ways.\n\n*  (https://pypi.org/project/pcser)\n\n ```bash\n conda create --name pcser python=3.11\n \n conda activate pcser\n \n pip install pcser --upgrade\n ```\n\n* \n\n ```bash\n conda create --name pcser python=3.11\n \n conda activate pcser\n \n git clone https://github.com/2003100127/pcser.git\n \n cd pcser\n \n pip install .\n ```\n\n## \ud83d\ude80 Quick start\n\n```python\nimport pcser as pcs\n\npcs.load.evaluate(\n data_ref_fpn='./Proteomics_07262023_rv_C57BL6_spl54.xlsx',\n sv_fp='./', # None to('data/')\n input_fpn='./example.xlsx',\n model_fpn='./best_cv.joblib',\n sheet_name='a', # a b\n # mfi_ref=[10271.33333, 10747, 10303.33333, 9663.333333, 10056],\n mfi_ref=[3606.333333, 3606.333333, 3606.333333, 3606.333333],\n\n # is_norm=True,\n # norm_met='minmax', # minmax std maxabs\n # mode='compo', # compo annot\n # mark='spl54', # spl54 spl63\n # version='extended', # extended old\n)\n```\n\nThen, it outputs what is shown below.\n\n```python\n# You are using extended sheets.\n# You have selected the minmax normalization method.\n# Data summary:\n# Number of samples: 54\n# Number of features: 419\n# You have the samples: ['HuApoA1', 'MoApoA1', 'HuClusterin', 'MoClusterin']\n# PCSER predictions: \n# stealth_effect MFI\n# HuApoA1 0.670762 3099.790003\n# MoApoA1 0.662108 3189.458730\n# HuClusterin 0.634621 3474.270396\n# MoClusterin 0.633914 3481.599008\n# stealth_effect\tMFI\n# HuApoA1\t0.670762\t3099.790003\n# MoApoA1\t0.662108\t3189.458730\n# HuClusterin\t0.634621\t3474.270396\n# MoClusterin\t0.633914\t3481.599008\n```\n\n## \ud83d\udcc4 Citation\n```angular2html\n@article{PCSER,\n title = {PCSER},\n author = {Jianfeng Sun},\n doi = {xxx},\n url = {https://github.com/2003100127/pcser},\n journal = {xxx}\n year = {2024},\n}\n```\n\n## \ud83c\udfe0 Homepage\n[\ud83d\udccdOxford University](https://www.ndorms.ox.ac.uk/team/jianfeng-sun) \n\n## \ud83d\udce7 Reach us\n[](https://www.linkedin.com/in/jianfeng-sun-2ba9b1132) \n[](mailto:jianfeng.sunmt@gmail.com)\n[](mailto:jianfeng.sun@ndorms.ox.ac.uk)\n<a href=\"https://twitter.com/Jianfeng_Sunny\" ><img src=\"https://img.shields.io/twitter/follow/Jianfeng_Sunny.svg?style=social\" /> </a>",
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