Skip to content
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ You can always check which modules are installed/available to you by running `ge
### Composite / Ensemble Utilities

1. `ZScoreRescaledScorer`
- Wraps any per-variant scorer and rescales its log-ratios within an amino-acid group (destination residue or substitution type) — the z-score ("Z") ranking from the [MULTI-evolve](https://www.science.org/doi/10.1126/science.adr8628) ensemble. Behaves as a single scikit-learn transformer (`fit`/`transform`), and `score_table` returns the per-mutation DataFrame.
- Wraps any per-variant scorer and rescales its log-ratios within an amino-acid group (destination residue or substitution type) — the z-score ("Z") ranking from the [MULTI-evolve](https://www.science.org/doi/10.1126/science.aea1820) ensemble. Behaves as a single scikit-learn transformer (`fit`/`transform`), and `score_table` returns the per-mutation DataFrame.
- See the "Ensemble Variant Nomination" user guide for a full structure + sequence first-round scan. Lower-level primitives (`per_variant_mutation_info`, `zscore_by_aa_group`) live in `aide_predict.utils.scoring`.

### Embeddings for Downstream ML
Expand Down
2 changes: 1 addition & 1 deletion docs/user_guide/multi_evolve_ensemble.md
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ title: Ensemble Variant Nomination

## Overview

This guide reproduces the first round of the [MULTI-evolve](https://www.science.org/doi/10.1126/science.adr8628) zero-shot nomination workflow using AIDE primitives. The idea is to score a full saturation-mutagenesis (SSM) library along two complementary tracks and combine them:
This guide reproduces the first round of the [MULTI-evolve](https://www.science.org/doi/10.1126/science.aea1820) zero-shot nomination workflow using AIDE primitives. The idea is to score a full saturation-mutagenesis (SSM) library along two complementary tracks and combine them:

- a **structure track** — `ESMIFLikelihoodWrapper` (ESM-IF1), conditioned on the backbone;
- a **sequence track** — an ensemble of sequence PLMs (`ESM2LikelihoodWrapper` loading the ESM-1v / ESM-2 checkpoints), averaged.
Expand Down
Loading