| --- |
| license: mit |
| tags: |
| - genomics |
| - dna |
| - variant-effect-prediction |
| - cardiomyopathy |
| - evo2 |
| base_model: arcinstitute/evo2_40b |
| --- |
| |
| # Supervised Evo 2 classifier for SNV pathogenicity |
|
|
| Classifier of ClinVar pathogenic/likely pathogenic (P/LP) versus benign/likely benign (B/LB) coding SNVs in 24 |
| cardiomyopathy genes, from Evo 2 40B embeddings. It is the supervised SNV classifier of *Evo 2 Predicts |
| Cardiomyopathy-Associated Variants and Elucidates Their Underlying Mechanisms* (Kurozumi, Otsuka et al.; bioRxiv |
| doi:10.64898/2026.05.15.725304), Supplementary Methods and Figure S2A. |
|
|
| ## Input |
|
|
| For each SNV, a 128-bp window with the variant at the 65th position (GRCh38) is embedded with Evo 2 40B |
| (`arcinstitute/evo2_40b`, layer `blocks.20.mlp.l3`) for four sequences: reference, its reverse complement, |
| alternate, its reverse complement. Each embedding is averaged over the 128 positions (8,192 values) and the four are |
| concatenated (32,768 values), then standardised with `scaler_mean` / `scaler_scale` stored in the checkpoint. |
|
|
| ## Model and training |
|
|
| - Input (32,768) → Linear (512) → ReLU → BatchNorm → Dropout (0.3) → Linear (128) → ReLU → BatchNorm → Dropout (0.3) |
| → Linear (32) → ReLU → BatchNorm → Dropout (0.3) → Linear (1); the output is a logit (sigmoid gives the probability). |
| - Data: all 22,840 coding SNVs (3,535 P/LP, 19,305 B/LB), split at the SNV level into training 14,617, validation |
| 3,655 and test 4,568 by two stratified random splits (scikit-learn, random_state 42). |
| - Adam (learning rate 3 × 10⁻⁴, batch 128), binary cross-entropy, up to 500 epochs; early stopping (patience 100) on |
| the validation AUROC; learning-rate decay (×0.5, patience 20, minimum 10⁻⁶) on the validation loss; gradient |
| clipping 1.0; seed 42. The weights of the epoch with the highest validation AUROC (epoch 29) are given here. |
| |
| ## Test performance (n = 4,568) |
| |
| | AUROC | AUPRC | |
| |---|---| |
| | 0.996 | 0.982 | |
| |
| `test_predictions.csv` lists every test SNV with its predicted probability. |
|
|
| ## Usage |
|
|
| ```python |
| import torch, torch.nn as nn |
| |
| class SNVClassifier(nn.Module): |
| def __init__(self, d_in=32768, p=0.3): |
| super().__init__() |
| self.fc1, self.bn1, self.dropout1 = nn.Linear(d_in, 512), nn.BatchNorm1d(512), nn.Dropout(p) |
| self.fc2, self.bn2, self.dropout2 = nn.Linear(512, 128), nn.BatchNorm1d(128), nn.Dropout(p) |
| self.fc3, self.bn3, self.dropout3 = nn.Linear(128, 32), nn.BatchNorm1d(32), nn.Dropout(p) |
| self.fc4 = nn.Linear(32, 1) |
| def forward(self, x): |
| x = self.dropout1(self.bn1(torch.relu(self.fc1(x)))) |
| x = self.dropout2(self.bn2(torch.relu(self.fc2(x)))) |
| x = self.dropout3(self.bn3(torch.relu(self.fc3(x)))) |
| return self.fc4(x).squeeze(-1) |
| |
| ck = torch.load("snv_classifier_22840.pth", map_location="cpu", weights_only=True) |
| model = SNVClassifier(); model.load_state_dict(ck["model_state_dict"]); model.eval() |
| prob = torch.sigmoid(model((x - ck["scaler_mean"]) / ck["scaler_scale"])) # x: (n, 32,768) as described above |
| ``` |
|
|
| ## Files |
|
|
| - `snv_classifier_22840.pth`: weights and feature standardisation |
| - `test_predictions.csv`, `metrics.json`, `history.csv` |
|
|
| Also archived on Zenodo with the analysis code: doi:10.5281/zenodo.23824204. |
|
|
| ## Citation |
|
|
| ``` |
| @article{kurozumi2026evo2, |
| title={Evo 2 Predicts Cardiomyopathy-Associated Variants and Elucidates Their Underlying Mechanisms}, |
| author={Kurozumi, Atsumasa and Otsuka, Naoto and Ito, Masamichi and Kawakami, Toshinaru and Isagawa, Takayuki and Kodera, Satoshi and Takeda, Norihiko}, |
| journal={bioRxiv}, year={2026}, doi={10.64898/2026.05.15.725304} |
| } |
| ``` |
|
|
