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--- |
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license: apache-2.0 |
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tags: |
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- glycan |
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- glycomics |
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- glycoinformatics |
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- mass_spectrometry |
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--- |
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## Model Details |
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**Model Name:** GlycoBERT |
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**Model Type:** Transformer-based sequence classifier for glycan structure prediction |
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**Architecture:** BERT (Bidirectional Encoder Representations from Transformers) |
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**Version:** 1.0 |
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**Date:** 6/25/2025 |
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### Model Description |
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GlycoBERT is a transformer-based deep learning model designed to predict glycan structures from tandem mass spectrometry (MS/MS) data. The model treats mass spectra as tokenized sequences ("MS sentences") and performs multi-class classification to assign spectra to one of 3,590 possible glycan structure classes. |
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### Architecture Details |
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- **Base Architecture:** BertForSequenceClassification |
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- **Parameters:** 96 million |
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- **Layers:** 12 transformer layers |
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- **Attention Heads:** 12 per layer |
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- **Hidden Size:** 768 dimensions |
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- **Max Sequence Length:** 512 tokens |
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- **Vocabulary Size:** 10,010 tokens |
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## Intended Use |
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### Primary Use Cases |
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- Glycan structure prediction from MS/MS spectra |
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- High-throughput glycomics analysis |
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- Structural annotation of mass spectrometry data |
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- Research applications in glycobiology and glycoinformatics |
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## Ethical Considerations |
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### Responsible Use |
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- Model predictions should be validated experimentally |
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- Not intended for direct clinical decision-making without proper validation |
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- Users should understand the model's limitations and scope |
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### Potential Risks |
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- Over-reliance on computational predictions without experimental validation |
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- Misinterpretation of confidence scores as absolute certainty |
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- Application to data significantly different from training distribution |
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## Model Version |
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- **GlycoBERT-F:** Version trained on full dataset |
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### Code and Data Availability |
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- **Repository:** [GitHub (glycotrans)](https://github.com/cabsel/glycotrans) |
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- **Training Data:** [](https://doi.org/10.5281/zenodo.15741423) |
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- **Example Inference:** [Google Colab](https://colab.research.google.com/drive/1otVLVDQfLyldtIFcBxGnwVf9PeeTnJ17?usp=sharing) |
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