protein corona stealth effect prediction
Project description
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)
conda create --name pcser python=3.11 conda activate pcser pip install pcser --upgrade
-
conda create --name pcser python=3.11 conda activate pcser git clone https://github.com/2003100127/pcser.git cd pcser pip install .
🚀 Quick start
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.
# 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
@article{PCSER,
title = {PCSER},
author = {Jianfeng Sun},
doi = {xxx},
url = {https://github.com/2003100127/pcser},
journal = {xxx}
year = {2024},
}
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