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# Data | ||
example/ | ||
temp/*.png | ||
temp/ | ||
*.mgf | ||
*.msp | ||
*.npy | ||
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# DeepMASS2 | ||
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DeepMASS2 is a cross-platform GUI software tool, which enables deep-learning based metabolite annotation | ||
via semantic similarity analysis of mass spectral language (DeepMASS). This approach enables the prediction | ||
of structurally related metabolites for the unknown compounds. By considering the chemical space, these | ||
structurally related metabolites provide valuable information about the potential location of the unknown | ||
metabolites and assist in ranking candidates obtained from molecular structure databases. | ||
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## Installation | ||
Please follow the following installation steps: | ||
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1. Install [Anaconda](https://www.anaconda.com/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html) | ||
2. Create a new conda environment and activate: | ||
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conda create -n deepmass python=3.8.13 | ||
conda activate deepmass | ||
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3. Clone the repository and enter: | ||
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git clone https://github.com/hcji/DeepMASS2_GUI.git | ||
cd DeepMASS2_GUI | ||
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4. Install dependency: | ||
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pip install -r requirements.txt | ||
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5. Run DeepMASS | ||
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python DeepMASS2.py | ||
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## Release | ||
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In preparation | ||
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## Documentation | ||
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In preparation | ||
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## Citation | ||
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In preparation | ||
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## Contact | ||
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Ji Hongchao | ||
E-mail: [email protected] | ||
<div itemscope itemtype="https://schema.org/Person"><a itemprop="sameAs" content="https://orcid.org/0000-0002-7364-0741" href="https://orcid.org/0000-0002-7364-0741" target="orcid.widget" rel="me noopener noreferrer" style="vertical-align:top;"><img src="https://orcid.org/sites/default/files/images/orcid_16x16.png" style="width:1em;margin-right:.5em;" alt="ORCID iD icon">https://orcid.org/0000-0002-7364-0741</a></div> | ||
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WeChat public account: Chemocoder | ||
<img align="center" src="https://github.com/hcji/hcji/blob/main/img/qrcode.jpg" width="20%"/> |
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adjustText==0.7.3 | ||
gensim==4.2.0 | ||
matchms==0.17.0 | ||
matplotlib==3.5.3 | ||
molmass==2021.6.18 | ||
ms2deepscore==0.2.3 | ||
networkx==2.8.6 | ||
numba==0.56.2 | ||
numpy==1.23.3 | ||
pandas==1.4.4 | ||
pyinstaller==5.5 | ||
rdkit-pypi | ||
scikit-learn==1.1.3 | ||
scikit-opt==0.6.6 | ||
scipy==1.9.3 | ||
seaborn==0.12.1 | ||
spec2vec==0.6.0 | ||
tqdm==4.64.1 | ||
PyQt5==5.15.7 | ||
hnswlib==0.6.2 | ||
JPype1==1.4.1 | ||
beautifulsoup4==4.11.1 |