| --- |
| license: mit |
| library_name: pytorch-lightning |
| pipeline_tag: tabular-classification |
| tags: |
| - biology |
| - genomics |
| datasets: |
| - Genentech/human-chromhmm-fullstack-data |
| base_model: |
| - Genentech/enformer-model |
| --- |
| |
| # human-chromhmm-fullstack-model |
|
|
| ## Model Description |
| This model is a multi-class classifier trained to predict chromatin state annotations for genomic DNA sequences. It classifies sequences into 16 chromatin states based on the ChromHMM fullstack annotation. It was trained by fine-tuning the Enformer model using the `grelu` library. |
|
|
| - **Architecture:** Fine-tuned Enformer (EnformerPretrainedModel) |
| - **Input:** Genomic sequences (hg38) |
| - **Output:** Probability distribution over 16 chromatin states |
| - **Parameters:** 71.5M total (all trainable) |
|
|
| ### Chromatin States |
| Acet, BivProm, DNase, EnhA, EnhWk, GapArtf, HET, PromF, Quies, ReprPC, TSS, Tx, TxEnh, TxEx, TxWk, znf |
|
|
| ## Performance |
|
|
| Metrics are computed per chromatin state and averaged across all 16 states. |
|
|
| ### Test Set |
| | Metric | Mean | Std | Min | Max | |
| |--------|------|-----|-----|-----| |
| | Accuracy | 0.4373 | 0.2162 | 0.2455 | 0.8528 | |
| | AUROC | 0.8609 | 0.0767 | 0.7652 | 0.9952 | |
| | Average Precision | 0.4113 | 0.1974 | 0.1362 | 0.8015 | |
|
|
| ### Validation Set |
| | Metric | Mean | Std | Min | Max | |
| |--------|------|-----|-----|-----| |
| | Accuracy | 0.4487 | 0.2098 | 0.2164 | 0.8696 | |
| | AUROC | 0.8654 | 0.0763 | 0.7594 | 0.9950 | |
| | Average Precision | 0.4155 | 0.1848 | 0.1241 | 0.7812 | |
|
|
| ### Per-class Test Metrics |
| | State | Accuracy | AUROC | AvgPrec | |
| |-------|----------|-------|---------| |
| | Acet | 0.2939 | 0.7973 | 0.2091 | |
| | BivProm | 0.5431 | 0.9373 | 0.3575 | |
| | DNase | 0.8528 | 0.9905 | 0.7527 | |
| | EnhA | 0.2950 | 0.8145 | 0.3368 | |
| | EnhWk | 0.2683 | 0.8144 | 0.2947 | |
| | GapArtf | 0.7988 | 0.9517 | 0.7029 | |
| | HET | 0.2455 | 0.8236 | 0.4982 | |
| | PromF | 0.5940 | 0.9557 | 0.6369 | |
| | Quies | 0.3662 | 0.8512 | 0.3610 | |
| | ReprPC | 0.2874 | 0.7652 | 0.2522 | |
| | TSS | 0.8302 | 0.9952 | 0.8015 | |
| | Tx | 0.2590 | 0.8072 | 0.3197 | |
| | TxEnh | 0.2694 | 0.8252 | 0.2770 | |
| | TxEx | 0.5336 | 0.8821 | 0.3563 | |
| | TxWk | 0.2510 | 0.7781 | 0.2880 | |
| | znf | 0.3079 | 0.7851 | 0.1362 | |
|
|
| ## Training Details |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | Task | Multiclass classification | |
| | Loss | Binary Cross-Entropy (with class weights) | |
| | Optimizer | Adam | |
| | Learning rate | 0.0001 | |
| | Batch size | 512 | |
| | Max epochs | 10 | |
| | Devices | 4 | |
| | n_transformers | 1 | |
| | crop_len | 0 | |
| | grelu version | 1.0.4.post1.dev39 | |
|
|
| ## Repository Content |
| 1. `model.ckpt`: The trained model weights and hyperparameters (PyTorch Lightning checkpoint). |
| 2. `2_train.ipynb`: Jupyter notebook containing the training logic, architecture definition, and evaluation loops. |
| 3. `output.log`: Training logs. |
|
|
| ## How to use |
| To load this model for inference or fine-tuning, use the `grelu` interface: |
|
|
| ```python |
| from grelu.lightning import LightningModel |
| from huggingface_hub import hf_hub_download |
| |
| ckpt_path = hf_hub_download( |
| repo_id="Genentech/human-chromhmm-fullstack-model", |
| filename="model.ckpt" |
| ) |
| |
| model = LightningModel.load_from_checkpoint(ckpt_path, weights_only=False) |
| model.eval() |
| ``` |
|
|
