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--- |
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license: mit |
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metrics: |
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- accuracy |
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tags: |
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- biology |
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pipeline_tag: text-classification |
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--- |
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# Model description |
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**PPPSL**(PPPSL, Prediction of prokaryotic protein subcellular localization) is a protein language model fine-tuned from [**ESM2**](https://github.com/facebookresearch/esm) pretrained model [(***facebook/esm2_t36_3B_UR50D***)](https://huggingface.co/facebook/esm2_t36_3B_UR50D) on a prokaryotic protein subcellular localization dataset. |
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**PPPSL** achieved the following results: |
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Train Loss: 0.0148 |
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Train Accuracy: 0.9923 |
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Validation Loss: 0.0718 |
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Validation Accuracy: 0.9893 |
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Epoch: 20 |
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# The dataset for training **PPPSL** |
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The full dataset contains 11,970 protein sequences, including Cellwall (87), Cytoplasmic (6,905), CYtoplasmic Membrane (2,567), Extracellular (1,085), Outer Membrane (758), and Periplasmic (568). |
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The highly imbalanced sample sizes across the six categories in this dataset pose a significant challenge for classification. |
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The dataset was downloaded from the website at [**DeepLocPro - 1.0**](https://services.healthtech.dtu.dk/services/DeepLocPro-1.0/). |
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# Model training code at GitHub |
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https://github.com/pengsihua2023/PPPSL-ESM2 |
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# How to use **PPPSL** |
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### An example |
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Pytorch and transformers libraries should be installed in your system. |
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### Install pytorch |
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``` |
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pip install torch torchvision torchaudio |
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``` |
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### Install transformers |
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``` |
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pip install transformers |
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``` |
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### Run the following code |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# Load the fine-tuned model and tokenizer |
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model_name = "sihuapeng/PPPSL-ESM2" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Sample protein sequence |
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protein_sequence = "MSKKVLITGGAGYIGSVLTPILLEKGYEVCVIDNLMFDQISLLSCFHNKNFTFINGDAMDENLIRQEVAKADIIIPLAALVGAPLCKRNPKLAKMINYEAVKMISDFASPSQIFIYPNTNSGYGIGEKDAMCTEESPLRPISEYGIDKVHAEQYLLDKGNCVTFRLATVFGISPRMRLDLLVNDFTYRAYRDKFIVLFEEHFRRNYIHVRDVVKGFIHGIENYDKMKGQAYNMGLSSANLTKRQLAETIKKYIPDFYIHSANIGEDPDKRDYLVSNTKLEATGWKPDNTLEDGIKELLRAFKMMKVNRFANFN" |
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# Encode the sequence as model input |
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inputs = tokenizer(protein_sequence, return_tensors="pt") |
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# Perform inference using the model |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Get the prediction result |
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logits = outputs.logits |
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predicted_class_id = torch.argmax(logits, dim=-1).item() |
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id2label = {0: 'CYtoplasmicMembrane', 1: 'Cellwall', 2: 'Cytoplasmic', 3: 'Extracellular', 4: 'OuterMembrane', 5: 'Periplasmic'} |
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predicted_label = id2label[predicted_class_id] |
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# Output the predicted class |
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print ("===========================================================================================================================================") |
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print(f"Predicted class Label: {predicted_label}") |
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print ("===========================================================================================================================================") |
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``` |
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## Funding |
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This project was funded by the CDC to Justin Bahl (BAA 75D301-21-R-71738). |
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### Model architecture, coding and implementation |
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Sihua Peng |
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## Group, Department and Institution |
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### Lab: [Justin Bahl](https://bahl-lab.github.io/) |
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### Department: [College of Veterinary Medicine Department of Infectious Diseases](https://vet.uga.edu/education/academic-departments/infectious-diseases/) |
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### Institution: [The University of Georgia](https://www.uga.edu/) |
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