MODEL LEARNING PATH

Enformer

Enformer compresses a long DNA window with convolution and learned pooling, then uses relative attention to connect distant sequence features. Species-specific heads predict central assay profiles.

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A landmark in bringing long-range attention into sequence-to-regulatory-function modeling, with Borzoi explicitly building on its core architecture. Its original benchmark gains should be read alongside a 2023 independent evaluation showing weak prediction of expression variation between personal genomes.

Chapters

  1. Compress sequence context

    The convolutional stem and pooling tower convert bases into wider, lower-resolution sequence features.

    Track how local sequence features become lower-resolution pooled representations.

  2. Connect distant regions

    Transformer blocks use signed genomic-distance features alongside content attention.

    Combine sequence-content attention with signed genomic-distance information.

  3. Predict the central profiles

    Crop the contextual representation and apply the species-specific output heads.

    Connect the central contextual representation to assay-specific output profiles.

  4. Learn from assay counts

    Count-valued labels supervise the predicted assay profiles.

    Apply the masked count objective to valid observed assay values.

All exercises in this guide