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--- |
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license: mit |
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tags: |
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- biology |
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- protein-classification |
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- microalgae |
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- genomics |
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- nanoGPT |
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datasets: |
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- custom |
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pipeline_tag: text-classification |
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--- |
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# Model Card: algaGPT |
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## Overview |
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**Name:** algaGPT |
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**Type:** Causal language model for protein sequence classification |
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**Base:** nanoGPT (Andrej Karpathy) |
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**Task:** Binary classification of microalgal vs. contaminant protein sequences |
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**Mode:** TI-inclusive (full-length sequences) |
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## Training |
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- **Data:** ~58.6M protein sequences (1:1 algal:contaminant ratio) |
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- **Algal sources:** 166 microalgal genomes across 10 phyla |
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- **Contaminant sources:** Bacterial, archaeal, and fungal sequences from NCBI nr |
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## Performance |
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| Metric | Score | |
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|--------|-------| |
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| Recall | >99% | |
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| Speed vs BLASTP+ | ~10,701× faster | |
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## Usage |
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Input a protein sequence; model generates a classification tag (algal/conta (contaminant)) via next-token prediction. |
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## Citation |
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Nelson DR, Jaiswal AK, Ismail NS, Mystikou A, Salehi-Ashtiani K. Pan-microalgal dark proteome mapping via interpretable deep learning and synthetic chimeras. *Patterns*. 2024;6(11). |
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## Contact |
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Kourosh Salehi-Ashtiani |
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ksa3@nyu.edu |
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