biotrainer 1.4.0

Biological prediction models made simple.

Biotrainer

Biological prediction models made simple.

Overview

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

Quick Start

1. Installation

Install using pip:

pip install biotrainer

Manual 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

2. Basic Usage

# Training
biotrainer train --config examples/sequence_to_class/config.yml

# Inference
python3
>>> from biotrainer.inference import Inferencer
>>> inferencer, _ = Inferencer.create_from_out_file('output/out.yml')
>>> predictions = inferencer.from_embeddings(your_embeddings)

3. Quick Start Datasets

• Subcellular Localization Prediction
  • Protocol: sequence_to_class/residues_to_class
  • Citations and Download
• Secondary Structure Prediction
  • Protocol: residue_to_class
  • Citations and Download

Features

Supported Prediction Tasks

• 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)

Built-in Capabilities

• Multiple embedding methods (ProtT5, ESM-2, ONNX, 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

Autoeval

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 docs or in the autoeval examples.

Documentation

Tutorials

• First Steps Guide
• Interactive Tutorials
• Config Options Overview
• Biocentral Web Interface

Detailed Guides

• Data Standards
• Configuration Options
• Troubleshooting

Example Configuration

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

Docker Support

# 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.yml

More information on running docker with gpus: Nvidia container toolkit

Getting Help

• Check our Troubleshooting Guide
• Create an issue
• Visit biocentral.cloud

Citation

@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}
}