Prediction - deepsmirud

Overview¶

You need to decompress the example_data.zip file in your preferred folder, e.g., deepsmirud/.

There are three required files.

br_sm_mirna.txt
br_smile.txt
path to *.fasta

We display 4 pairs of small molecules and miRNAs to predict their possible regulation types.

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sm	mirna
5743	MIMAT0000539
84093	MIMAT0009203
148124	MIMAT0000098
5757	MIMAT0000066

Python¶

We access DeepsmirUD by defining the following parameters.

params = {
    'br_fpn': '../../data/deepsmirud/example_data/br_sm_mirna.txt',
    'smile_fpn': '../../data/deepsmirud/example_data/br_smile.txt',
    'fasta_fp': '../../data/deepsmirud/example_data/',
    'method': 'LSTMCNN',
    'model_fp': '../../data/deepsmirud/model/lstmcnn',
    'sv_fpn': '../../data/deepsmirud/example_data/pred.deepsmirud',
}

Command

Output log

import deepsmirud

deepsmirud.predict.sm_mir_regulation_type(
    br_fpn=params['br_fpn'],
    smile_fpn=params['smile_fpn'],
    fasta_fp=params['fasta_fp'],
    method=params['method'],
    model_fp=params['model_fp'],
    sv_fpn=params['sv_fpn'],
)

 ____                                _      _   _ ____  
|  _ \  ___  ___ _ __  ___ _ __ ___ (_)_ __| | | |  _ \ 
| | | |/ _ \/ _ \ '_ \/ __| '_ ` _ \| | '__| | | | | | |
| |_| |  __/  __/ |_) \__ \ | | | | | | |  | |_| | |_| |
|____/ \___|\___| .__/|___/_| |_| |_|_|_|   \___/|____/ 
                |_|                                     

05/04/2025 12:03:09 logger: =>Prediction starts...
05/04/2025 12:03:09 logger: small-molecule smile map:
       sm                                              smile
0    5743  C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@@]4([C@...
1   84093  CC1=CN(C(=O)NC1=O)C2CC(C(O2)COP(=O)(O)OC3CC(OC...
2  148124  CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@...
3    5757  C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@@H]2O)CCC4=C3...
    prob_up       pred_type
0  0.050344  Downregulation
1  0.984405    Upregulation
2  0.998275    Upregulation
3  1.000000    Upregulation

CLI¶

DeepsmirUD can also be used in shell. To know how to use, please type

deepsmirud -h

It shows the usage of different parameters.

-m, --method,
    A deep learning method. It can be any below.
    AlexNet | BiRNN | RNN | Seq2Seq |
    CNN | ConvMixer64 | DSConv | LSTMCNN |
    MobileNet | ResNet18 | ResNet50 | SEResNet
-br, --br_fpn, binary relations between small molecules and mirnas
-sm, --smile_fpn, map between small molecule IDs and their smile strings
-mir, --fasta_fp, miRNA fasta file paths
-mf, --model_fp, a model path
-o, --sv_fpn, outputting deepsmirud predictions

You can run it using the following code.

Command

Output

deepsmirud -m LSTMCNN -br ./data/deepsmirud/example_data/br_sm_mirna.txt -sm ./data/deepsmirud/example_data/br_smile.txt -mir ./data/deepsmirud/example_data/ -mf ./data/deepsmirud/model/lstmcnn -o ./data/deepsmirud/out.deepsmirud

 ____                                _      _   _ ____
|  _ \  ___  ___ _ __  ___ _ __ ___ (_)_ __| | | |  _ \
| | | |/ _ \/ _ \ '_ \/ __| '_ ` _ \| | '__| | | | | | |
| |_| |  __/  __/ |_) \__ \ | | | | | | |  | |_| | |_| |
|____/ \___|\___| .__/|___/_| |_| |_|_|_|   \___/|____/
                |_|

05/04/2025 12:08:12 logger: =>Prediction starts...
05/04/2025 12:08:12 logger: small-molecule smile map:
       sm                                              smile
0    5743  C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@@]4([C@...
1   84093  CC1=CN(C(=O)NC1=O)C2CC(C(O2)COP(=O)(O)OC3CC(OC...
2  148124  CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@...
3    5757  C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@@H]2O)CCC4=C3...
[12:08:12] DEPRECATION WARNING: please use MorganGenerator
[12:08:12] DEPRECATION WARNING: please use MorganGenerator
[12:08:12] DEPRECATION WARNING: please use MorganGenerator
[12:08:12] DEPRECATION WARNING: please use MorganGenerator
    prob_up       pred_type
0  0.050344  Downregulation
1  0.984405    Upregulation
2  0.998275    Upregulation
3  1.000000    Upregulation

References¶

Sun, J., Ru, J., Ramos-Mucci, L., Qi, F., Chen, Z., Chen, S., Cribbs, A. P., Deng, L., & Wang, X. (2023). DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning. International Journal of Molecular Sciences, 24(3). 10.3390/ijms24031878

Tutorial

Model