Update model card: full Patterns author list, ELF-NET application note

README.md

@@ -1,29 +1,26 @@
 ---
 license: mit
 tags:
-  - biology
-  - protein-classification
-  - microalgae
-  - genomics
-  - nanoGPT
-datasets:
-  - custom
+- biology
+- protein-classification
+- microalgae
+- genomics
+- nanoGPT
+- metagenomics
+- tara-oceans
 pipeline_tag: text-classification
 ---
 
-# Model Card: algaGPT
+# algaGPT
 
-## Overview
+Causal language model for binary classification of microalgal vs. contaminant protein sequences.
 
-**Name:** algaGPT
-**Type:** Causal language model for protein sequence classification
-**Base:** nanoGPT (Andrej Karpathy)
-**Task:** Binary classification of microalgal vs. contaminant protein sequences
-**Mode:** TI-inclusive (full-length sequences)
+## Model Description
 
-## Training
-
-- **Data:** ~58.6M protein sequences (1:1 algal:contaminant ratio)
+- **Architecture:** nanoGPT (Andrej Karpathy)
+- **Task:** Binary classification of microalgal protein sequences via next-token prediction
+- **Mode:** TI-inclusive (full-length sequences)
+- **Training data:** ~58.6M protein sequences (1:1 algal:contaminant ratio)
 - **Algal sources:** 166 microalgal genomes across 10 phyla
 - **Contaminant sources:** Bacterial, archaeal, and fungal sequences from NCBI nr
 
@@ -32,18 +29,38 @@ pipeline_tag: text-classification
 | Metric | Score |
 |--------|-------|
 | Recall | >99% |
-| Speed vs BLASTP+ | ~10,701× faster |
+| Speed vs. BLASTp | ~10,701x faster |
 
 ## Usage
 
-Input a protein sequence; model generates a classification tag (algal/conta (contaminant)) via next-token prediction.
+**Input:** Protein sequence (amino acid string)
+**Output:** Classification tag (algal/contaminant) via next-token prediction
+
+## Applications
+
+algaGPT was used as the primary proteome extraction tool in the ELF-NET study (Nelson et al., forthcoming), where it purified algal protein sequences from 2,044 TARA Oceans metagenome assemblies, yielding 221.9 million sequences for downstream domain-environment coupling analysis.
+
+## Authors
+
+David R. Nelson, Ashish Kumar Jaiswal, Noha Samir Ismail, Alexandra Mystikou, Kourosh Salehi-Ashtiani
 
+Green Genomics Lab, New York University Abu Dhabi
 
 ## Citation
 
-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).
+```bibtex
+@article{la4sr2025,
+  title={Pan-microalgal dark proteome mapping via interpretable deep learning and synthetic chimeras},
+  author={Nelson, David R. and Jaiswal, Ashish Kumar and Ismail, Noha Samir and Mystikou, Alexandra and Salehi-Ashtiani, Kourosh},
+  journal={Patterns},
+  volume={6},
+  pages={101373},
+  year={2025},
+  publisher={Cell Press},
+  doi={10.1016/j.patter.2025.101373}
+}
+```
 
 ## Contact
 
-Kourosh Salehi-Ashtiani
-ksa3@nyu.edu
+Kourosh Salehi-Ashtiani — ksa3@nyu.edu