---
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)