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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.

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