STEAM_evolution_naive
=====================

.. sidebar:: Model Features

   - **Genome**: *mm10*
   - **Type**: Peak regression
   - **Parameters**: 6.3M
   - **Size**: ~62MB
   - **Input shape**: (2114, 4)
   - **Output shape**: (36,)

The **STEAM_evolution_naive** model is the mouse-only baseline from the evolutionary transfer learning study. It predicts cell-class-specific chromatin accessibility across 36 cell classes of the developing mouse embryo (E10–P0), trained solely on mouse chromatin accessibility data without any evolutionary information.

It is provided as the starting point of the STEAM progression (mouse-only → evolution-aware filtering → synteny-augmented :doc:`STEAM_v1 <steam_v1>`). 2114bp one-hot encoded DNA sequences are used to predict normalized Tn5 cut-site accessibility (log-scaled) over the central region of each window, per cell class.

The model is a CNN multiclass regression model using the :func:`~crested.tl.zoo.dilated_cnn` architecture. Like the other STEAM models, it maps DNA sequence directly to accessibility and is genome-agnostic at inference (applicable to any mammalian genome).

Details of the data and the model can be found in the original publication.

-------------------

.. admonition:: Citation

   Qiu, C., Daza, R.M., Welsh, I.C. et al. Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes (2026). https://doi.org/10.62329/hxkk6249

Usage
-------------------

.. code-block:: python
    :linenos:

    import crested
    import keras

    # download model
    model_path, output_names = crested.get_model("STEAM_evolution_naive")

    # load model
    model = crested.utils.load_model(model_path)

    # make predictions
    sequence = "A" * 2114
    predictions = crested.tl.predict(sequence, model)
    print(predictions.shape)