| --- |
| license: mit |
| tags: |
| - synthetic-lethality |
| - gene-encoder |
| - depmap |
| - masked-autoencoder |
| - cancer-biology |
| language: en |
| datasets: |
| - custom |
| pipeline_tag: feature-extraction |
| --- |
| |
| # SL-Predict: Frozen MAE Gene Encoder |
|
|
| Pretrained masked-autoencoder (MAE) gene encoder for cold-start synthetic lethality prediction from DepMap CRISPR screens. |
|
|
| ## Model Description |
|
|
| A 3-layer MLP encoder (1206 → 512 → 256 → 256) trained to reconstruct randomly masked DepMap Chronos dependency profiles (18,531 genes × 1,206 non-K562 cell lines) with MSE loss for 200 epochs. |
|
|
| **Key property:** This is the **leak-repaired** checkpoint — the 503-gene union of all downstream cold-start test sets was excluded from pretraining. TOST equivalence testing confirms the encoder is not load-bearing on pretrain–test gene overlap (p_max < 0.0001 at ±0.010 AUC). |
| |
| ## Performance |
| |
| When frozen and combined with LightGBM + confidence weighting on SynLethDB CRISPR/CRISPRi labels: |
| |
| | Metric | Value | |
| |--------|-------| |
| | Horlbeck K562 held-out AUC | **0.714 ± 0.018** (10-seed, gene-disjoint) | |
| | vs Published SOTA (SLMGAE) | +0.079 | |
| | vs Label-agreement ceiling | +0.015 | |
| |
| ## Usage |
| |
| ```python |
| import torch |
| |
| # Load checkpoint |
| ckpt = torch.load("mae_encoder_d256_leak_repaired.ckpt", map_location="cpu") |
| state_dict = ckpt["state_dict"] |
|
|
| # The encoder is the first 3 layers of the MAE |
| # Input: 1206-dim DepMap dependency profile (z-scored) |
| # Output: 256-dim gene embedding |
| ``` |
| |
| ## Training Details |
| |
| - **Data:** DepMap 26Q1 Chronos dependency profiles |
| - **Architecture:** MLP 1206→512→256→256 (encoder), mirror decoder |
| - **Objective:** Masked autoencoding (50% masking ratio, MSE loss) |
| - **Epochs:** 200 |
| - **Hardware:** Single NVIDIA A10G (Modal cloud), ~20 minutes |
| - **Leak repair:** 503 test-split genes excluded from pretraining data |
| |
| ## Citation |
| |
| ``` |
| @misc{large2026slpredict, |
| author = {Large, Jack}, |
| title = {Cold-start synthetic lethality prediction: Diagnosing evaluation inflation and a constructive baseline}, |
| year = {2026}, |
| url = {https://github.com/j8ckfi/sl-predict} |
| } |
| ``` |
| |
| ## Links |
| |
| - **Paper:** [GitHub](https://github.com/j8ckfi/sl-predict) |
| - **Code:** [https://github.com/j8ckfi/sl-predict](https://github.com/j8ckfi/sl-predict) |
| |
