bijux-proteomics-lab 0.3.2

Assay planning, schedule-aware experiment orchestration, and outcome interpretation for lab-in-the-loop proteomics

bijux-proteomics-lab

Package Family

bijux-proteomics-lab converts scientific requirements into executable assay plans, dependency-aware schedules, and outcome interpretation that feeds back into program progression decisions.

Use this package when you need lab-in-the-loop planning under gate constraints, capacity-aware batch construction, and rerun recommendations tied to assay outcomes.

Why teams pick this package

• practical lab planning built around dependencies, capacity, and timing limits
• structured assay plans and review packets ready for scientific operations
• outcome interpretation flows that support rerun and escalation decisions
• repository contracts for integrating plan queues and feedback loops

Typical use cases

• convert candidate requirements into executable assay schedules
• sequence assay batches while respecting constraints and review gates
• summarize execution outcomes and recommend reruns with explicit rationale
• track plan quality trends and feed outcomes back into decision pipelines

Installation

pip install bijux-proteomics-lab

Quick start

from bijux_proteomics_lab import planning, outcomes, repositories

Package boundaries

This package owns assay planning, schedule generation, outcome interpretation, and rerun strategy support.

It does not own program-stage authority, ranking policy, or evidence truth semantics.

Source guide

• src/bijux_proteomics_lab/planning.py for planning and scheduling models
• src/bijux_proteomics_lab/outcomes.py for outcome interpretation and rerun decisions
• src/bijux_proteomics_lab/repositories.py for repository contracts and trend summaries
• tests for executable behavior expectations

Documentation

• Package guide
• Ownership boundary
• Architecture overview
• Interface contracts
• Release and versioning