Biological prediction models made simple.
Biotrainer is an open-source framework that simplifies machine learning model development for protein analysis. It provides the following modules:
- Training: Easy to use training and inference pipelines for protein feature prediction
- Embedding: Built-in support for protein language models (ProtT5, ESM-2, ESM-C, ...) and baselines (blosum62, one_hot_encoding, ...)
- BioEngineer: Zero-shot predictions and mutation generation of protein sequences
- AutoEval: Automated evaluation of protein language models on curated downstream tasks
Install using pip:
pip install biotrainerManual installation using uv:
# First, install uv if you haven't already:
pip install uv
# Create and activate a virtual environment
uv venv
source .venv/bin/activate # On Unix/macOS
# OR
.venv\Scripts\activate # On Windows
# Basic installation
uv pip install -e .
# Installing with jupyter notebook support:
uv pip install -e ".[jupyter]"
# Installing with onnxruntime support (for onnx embedders and inference):
uv pip install -e ".[onnx-cpu]" # CPU version
uv pip install -e ".[onnx-gpu]" # CUDA version
uv pip install -e ".[onnx-mac]" # CoreML version (for Apple Silicon)
# You can also combine extras:
uv pip install -e ".[jupyter,onnx-cpu]"
# For Windows users with CUDA support:
# Visit https://pytorch.org/get-started/locally/ and follow GPU-specific installation, e.g.:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118# Training
biotrainer train --config examples/sequence_to_class/config.yml
# Inference after Training
python3
>>> from biotrainer.training import BiotrainerModel
>>> model = BiotrainerModel.from_training_result("output/out.yml")
>>> predictions = model.predict("SEQUENCE")
# AutoEval
autoeval_report = (AutoEval(embedder_name="facebook/esm2_t6_8M_UR50D", development_mode=True).
pbc_supervised_contact().
pbc_zeroshot_contact().
pbc_supervised().
pgym(zero_shot_method=ZeroShotMethod.MASKED_MARGINALS).
run())
autoeval_report.summary(development_mode=False)- Subcellular Localization Prediction
- Protocol:
sequence_to_class/residues_to_class - Citations and Download
- Protocol:
- Secondary Structure Prediction
- Protocol:
residue_to_class - Citations and Download
- Protocol:
- Residue-level classification (
residue_to_class) - Residue-level regression (
residue_to_value) [BETA] - Sequence-level classification (
sequence_to_class) - Sequence-level regression (
sequence_to_value) - Residues-level classification (
residues_to_class, like sequence_to_class with per-residue embeddings) - Residues-level regression (
residues_to_value, like sequence_to_value with per-residue embeddings)
The biotrainer autoeval module allows automatical evaluation of a protein language model on downstream tasks.
You can find public results (wip!) on the autoeval dashboard and compare them
to your own. Learn more in the autoeval examples.
protocol: residue_to_class
input_file: input.fasta
model_choice: CNN
optimizer_choice: adam
learning_rate: 1e-3
loss_choice: cross_entropy_loss
use_class_weights: True
num_epochs: 200
batch_size: 128
embedder_name: Rostlab/prot_t5_xl_uniref50# Run using pre-built image
docker run --gpus all --rm \
-v "$(pwd)/examples/docker":/mnt \
-u $(id -u ${USER}):$(id -g ${USER}) \
ghcr.io/sacdallago/biotrainer:latest /mnt/config.ymlMore information on running docker with gpus: Nvidia container toolkit
- Check our Troubleshooting Guide
- Create an issue
- Visit biocentral.cloud
@inproceedings{
sanchez2022standards,
title={Standards, tooling and benchmarks to probe representation learning on proteins},
author={Joaquin Gomez Sanchez and Sebastian Franz and Michael Heinzinger and Burkhard Rost and Christian Dallago},
booktitle={NeurIPS 2022 Workshop on Learning Meaningful Representations of Life},
year={2022},
url={https://openreview.net/forum?id=adODyN-eeJ8}
}