Fusion Strategies

Switch language: 融合策略

Enhancer-promoter prediction is not only about single-sequence encoding. It is also about how two encoded regions are combined before classification.

Supported strategies

  • concat

  • add

  • subtract

  • multiply

  • bilinear

  • concat_sub_mul

If enhancer and promoter embeddings are written as \(h_e\) and \(h_p\), the main fusion forms can be summarized as:

\[\mathrm{concat}: [h_e; h_p]\]
\[\mathrm{add}: h_e + h_p\]
\[\mathrm{subtract}: h_e - h_p\]
\[\mathrm{multiply}: h_e \odot h_p\]
\[\mathrm{concat\_sub\_mul}: [h_e; h_p; h_e - h_p; h_e \odot h_p]\]

Why fusion matters

Even a strong encoder can underperform if the pairwise interaction layer is too weak. Fusion determines how much relational information between enhancer and promoter embeddings is exposed to the classifier.

This is why fusion should not be treated as a minor implementation detail. It defines what kinds of pair structure the classifier is allowed to see directly.

Interpretation of the main options

  • concat keeps the two vectors intact and lets the classifier learn the interaction.

  • add emphasizes shared magnitude and symmetric aggregation.

  • subtract exposes directional difference.

  • multiply highlights element-wise agreement or co-activation.

  • bilinear offers a more expressive learned pairwise interaction.

  • concat_sub_mul explicitly combines identity, difference, and agreement signals in one representation.

Default choice

The repository uses concat_sub_mul as the default because it tends to be a balanced representation-rich option without requiring the parameter overhead of a full bilinear interaction.

Computational complexity

  • Time: fusion itself is usually a small fraction of total runtime compared with sequence encoding, except when bilinear interaction is used.

  • Memory: concatenation-based fusion enlarges classifier input width, while bilinear fusion increases parameter count more aggressively.

  • Best-fit regime: simple elementwise or concatenation-based fusion is adequate for broad benchmarking, whereas bilinear fusion is most useful when pairwise interaction expressivity is worth added cost.

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