Experiments

Switch language: 实验设计

Experiment philosophy

The project is designed as a structured benchmark rather than a single-model implementation. The experiment board in TASK.md organizes work into a model matrix, fusion ablations, and cross-cell-type generalization analysis.

Primary experiment matrix

The current plan tracks experiments E01 through E16:

  • E01: M1 CNN single-branch baseline

  • E02: M2 CNN dual-branch baseline

  • E03: M2 with concat_sub_mul fusion

  • E04: M3 k-mer + CNN

  • E05: M4 BiLSTM

  • E06: M6 standard Transformer

  • E07: M11 CNN + BiLSTM

  • E08: M12 CNN + Transformer

  • E09: M13 DNABERT-2 frozen

  • E10: M13 HyenaDNA fine-tune

  • E11: M9 Mamba

  • E12: M7 Linear Transformer

  • E13: M8 iTransformer

  • E14: M10 RWKV

  • E15: M5 mLSTM

  • E16: M14 MAE pretrain + fine-tune

Fusion ablation

The fusion strategy itself is a research axis. The documented ablation plan includes:

  • F01: concat

  • F02: add

  • F03: subtract

  • F04: multiply

  • F05: bilinear

  • F06: concat_sub_mul

Why this comparison matters

Enhancer-promoter prediction is naturally a pairwise learning problem. A strong encoder can still underperform if the interaction representation is weak. By explicitly exposing multiple fusion operators, the project separates two questions:

  • how expressive the sequence encoder is;

  • how effectively enhancer and promoter features are combined.

Seed strategy and reproducibility

Each experiment is intended to run on five random seeds:

  • seed 0

  • seed 1

  • seed 2

  • seed 3

  • seed 4

This reduces the risk of over-interpreting a favorable or unfavorable single run and gives a more stable estimate of expected behavior.

Expected outputs per experiment

Every seed directory stores:

  • configuration snapshot;

  • checkpoints;

  • training history;

  • test metrics;

  • ROC curve;

  • PR curve.

At the experiment level, summary statistics are intended to report mean and standard deviation across seeds.

Showcase value

This experiment design demonstrates more than model implementation. It shows:

  • baseline preservation;

  • controlled architectural expansion;

  • reproducible training organization;

  • explicit ablation thinking;

  • practical readiness for comparative sequence modeling studies.

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