Biotrainer is an open-source framework that simplifies machine learning model development for protein analysis. It provides:
- Easy-to-use training and inference pipelines for protein feature prediction
- Standardized data formats for various prediction tasks
- Built-in support for protein language models and embeddings
- Flexible configuration through simple YAML files
# Install using poetry (recommended)
poetry install
# Adding jupyter notebook (if needed):
poetry add jupyter
# 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
poetry run biotrainer config.yml
# Inference
python3
>>> from biotrainer.inference import Inferencer
>>> inferencer, out_file = Inferencer.create_from_out_file('output/out.yml')
>>> predictions = inferencer.from_embeddings(your_embeddings)- Residue-level classification (
residue_to_class) - Residues-level classification (
residues_to_class, like sequence_to_class with per-residue embeddings) - Sequence-level classification (
sequence_to_class) - Residues-level regression (
residues_to_value, like sequence_to_value with per-residue embeddings) - Sequence-level regression (
sequence_to_value)
- Multiple embedding methods (ProtT5, ESM, etc.)
- Various neural network architectures
- Cross-validation and model evaluation
- Performance metrics and visualization
- Sanity checks and automatic calculation of baselines (such as random, mean...)
- Docker support for reproducible environments
protocol: residue_to_class
sequence_file: sequences.fasta
labels_file: labels.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}
}