docs: rename AxoMEME to HyphAeon in model card

README.md

@@ -1,6 +1,6 @@
 ---
 license: mit
-library_name: axomeme
+library_name: hyphaeon
 tags:
 - biology
 - bioinformatics
@@ -11,19 +11,19 @@ tags:
 - onnx
 ---
 
-# AxoMEME
+# HyphAeon
 
 **Ultra-fast neural inference of episodic positive selection in molecular sequences.**
 
-AxoMEME detects site-level episodic diversifying positive selection (dN/dS > 1) across
+HyphAeon detects site-level episodic diversifying positive selection (dN/dS > 1) across
 arbitrary codon alignments and phylogenetic trees. It replaces numerical Maximum Likelihood
 Estimation with an axial geometric transformer using Continuous 4D Tree Rotary Position
 Embeddings (Tree-RoPE), reaching a 100x-1,000x speedup over HyPhy MEME and PAML CodeML while
 matching or exceeding their empirical statistical power at a strictly controlled false
 positive rate.
 
-- **Repository:** https://github.com/veg/axomeme
-- **Architecture notes:** [ARCHITECTURE.md](https://github.com/veg/axomeme/blob/main/ARCHITECTURE.md)
+- **Repository:** https://github.com/veg/HyphAeon
+- **Architecture notes:** [ARCHITECTURE.md](https://github.com/veg/HyphAeon/blob/main/ARCHITECTURE.md)
 - **Developed by:** Sergei L. Kosakovsky Pond and collaborators
 - **License:** MIT
 
@@ -33,13 +33,13 @@ The same model weights power three analyses, exposed as CLI subcommands:
 
 | Command | Analysis | Output |
 | :--- | :--- | :--- |
-| `predict` | **Episodic positive selection**: per-site MEME LRT surrogate (the core model). | per-site LRT + p-value |
+| `meme` | **Episodic positive selection**: per-site MEME LRT surrogate (the core model). | per-site LRT + p-value |
 | `epistasis` | **Epistatic sector mining (ESSM)**: phylogenetic branch attribution, inter-site co-selection networks, in-silico Selection Deep Mutational Scanning, and multi-scale epistatic sectors. | co-selection edges, sectors, DMS sweep (JSON / GraphML / CSV) |
 | `phenotype` | **Directional phenotype-genotype association (PhyloWAS)**: associates sites with a trait across the tree and extracts Phenotype-Associated Residue Signatures (PARS). Supports curated phenotype presets (marine, echolocation, high-altitude, longevity, …) or a user trait table. | per-site trait association, p-values, FDR |
 
 `epistasis` and `phenotype` are **derived analyses built on the selection model** (its per-site drive and
 branch-level attributions); they do not require separate weights. The results below and the variants
-concern the core `predict` model, which the other two consume.
+concern the core `meme` model (aliases: `predict`, `site-selection`), which the other two consume.
 
 ## Model variants
 
@@ -76,27 +76,27 @@ standard MEME null mixture, `0.5 * delta(0) + 0.5 * chi^2(1)`.
 Install the package, which pulls these weights automatically on first use:
 
 ```bash
-pip install git+https://github.com/veg/axomeme.git
+pip install git+https://github.com/veg/HyphAeon.git
 ```
 
 ### Command line
 
 ```bash
-axomeme predict \
+hyphaeon meme \
   --alignment my_genes.fasta \
   --tree my_genes.nwk \
   --output results.json \
   --csv results.csv
 ```
 
-If `--tree` is omitted, AxoMEME extracts an embedded tree from a NEXUS or FASTA alignment.
+If `--tree` is omitted, HyphAeon extracts an embedded tree from a NEXUS or FASTA alignment.
 
 ### Python
 
 ```python
-from axomeme import PhyloAxialTransformer, load_alignment_and_tree
+from hyphaeon import PhyloAxialTransformer, load_alignment_and_tree
 
-model = PhyloAxialTransformer.from_pretrained("datamonkey/axomeme").eval()
+model = PhyloAxialTransformer.from_pretrained("datamonkey/hyphaeon").eval()
 codons, aas, dists, mds, invariable, taxa, num_sites = load_alignment_and_tree(
     "my_genes.fasta", "my_genes.nwk"
 )
@@ -107,7 +107,7 @@ To load the **viral fine-tuned** variant explicitly:
 ```python
 import torch
 from safetensors.torch import load_file
-from axomeme import PhyloAxialTransformer
+from hyphaeon import PhyloAxialTransformer
 
 model = PhyloAxialTransformer(embed_dim=384, num_layers=6, num_heads=12,
                               window_size=1, num_thresholds=16)
@@ -133,7 +133,7 @@ flag. Sites that are invariable across the alignment are assigned an LRT of 0.
 Taking HyPhy MEME (p <= 0.10, asymptotic LRT >= 4.605) as ground truth across 84 empirical
 datasets from 9 independent literature studies (43,302 codons, up to 476 taxa):
 
-| Literature study & system | Datasets | Codons | ROC-AUC | PR-AUC | PPV | FPR | Spearman rho | HyPhy MLE | AxoMEME CPU | Speedup |
+| Literature study & system | Datasets | Codons | ROC-AUC | PR-AUC | PPV | FPR | Spearman rho | HyPhy MLE | HyphAeon CPU | Speedup |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
 | Abdul et al. (2018), SMC5/6 complex | 9 | 7,073 | 0.990 | 0.752 | 100.0% | 0.00% | 0.874 | 561.0 s | 3.84 s | 171.3x |
 | Nisson et al. (2025), CCDC137 (HIV Vpr) | 1 | 290 | 0.940 | 0.650 | 75.0% | 0.35% | 0.833 | 80.0 s | 0.21 s | 380.9x |
@@ -193,7 +193,7 @@ so browser or cross-language ports should account for this.
 
 ## Limitations and intended use
 
-AxoMEME is a research tool for comparative molecular evolution, intended as a fast screen for
+HyphAeon is a research tool for comparative molecular evolution, intended as a fast screen for
 sites under episodic diversifying selection.
 
 - It is trained to approximate HyPhy MEME, so it inherits that method's assumptions and is
@@ -215,10 +215,10 @@ sites under episodic diversifying selection.
 A publication is in preparation. Until it appears, cite the repository:
 
 ```bibtex
-@software{axomeme,
-  title = {AxoMEME: Ultra-Fast Neural Inference of Episodic Positive Selection in Molecular Sequences},
+@software{hyphaeon,
+  title = {HyphAeon: Ultra-Fast Neural Inference of Episodic Positive Selection in Molecular Sequences},
   author = {Kosakovsky Pond, Sergei L. and collaborators},
-  url = {https://github.com/veg/axomeme},
+  url = {https://github.com/veg/HyphAeon},
   year = {2026}
 }
 ```