Core data classes and I/O utilities for biotrainer.
Project description
biotrainer-core
A lightweight Python package providing shared data classes, I/O utilities, and helper functions for protein-related projects.
Purpose
biotrainer-core is designed as a minimal, dependency-light foundation that can be reused across multiple protein ML
projects (e.g. biotrainer) without pulling in heavy frameworks like PyTorch
or Transformers.
Features
Data Classes
Pydantic-based data models shared across protein ML workflows:
- Sequence data — representations for protein sequences and their metadata
- Protocols — supported task types (residue-level, sequence-level, contact prediction, …)
- Metrics — structured containers for evaluation metrics
- Model results & predictions — standardised output formats for biotrainer models
- Embedding statistics — summary statistics over embedding spaces
- AutoEval — data classes for automated evaluation tasks, reports, and benchmark datasets (FLIP, PBC)
H5 File Handling
Utilities for reading and writing HDF5 files used to store protein embeddings:
H5Database— high-level interface for embedding databases- Low-level helpers for chunked I/O and dataset management
Functions
Pure utility functions with no side effects:
- Hashing — deterministic sequence hashing
- Seeding — reproducible random seed management
- Ranking — ranking helpers for evaluation
- Bootstrapping — confidence interval estimation via bootstrapping
Input Files
- FASTA parsing — read and validate FASTA files into typed sequence objects
Installation
pip install biotrainer-core
License
MIT — see LICENSE for details.
Project details
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