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Midlife Microglial Trajectory Analyzer with Pydantic AI

NIH research demonstrates that brain immunity undergoes a major midlife overhaul, driven by distinct transcriptomic shifts in microglial populations. In this challenge, developers will construct a type-safe single-cell RNA velocity and trajectory interpretation agent using Pydantic AI. The system will ingest microglial cluster profile data, classify midlife neuroinflammatory transition markers, and output structured inflammatory state indices.

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What you are building

Construct a type-safe microglial trajectory analysis pipeline using Pydantic AI to characterize midlife brain immune shifts.

How work is evaluated

Evaluation

Evaluates state classification accuracy for microglial single-cell expression profiles.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

pydantic_validation

Model outputs parse into Pydantic target schema without errors

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

state_classification_accuracy

Accuracy of classified microglial activation state against expert labeled dataset • target: 0.88 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Define strict Pydantic schemas for microglial gene expression profiling

  • Execute Python bioinformatic tools for trajectory scoring within Pydantic AI dependencies

  • Classify midlife neuroinflammatory state transitions (homeostatic vs DAM/midlife-reactive)

  • Provide validated diagnostic reports on microglial age-dependent activation

Resources

Dataset notes

Single-cell RNA-seq microglial count matrices across rodent and human lifespan datasets (months 2 to 24).

Challenge setup

Evaluates state classification accuracy for microglial single-cell expression profiles.

Challenge input

JSON containing key gene marker levels (CD11b, CLEC7A, APOE, CX3CR1)

Pydantic AI

Mandatory Pydantic AI framework

Evaluated output

JSON containing microglial_state (string) and activation_index (float 0-1)

Evaluation checks
  • Model outputs parse into Pydantic target schema without errors
  • Accuracy of classified microglial activation state against expert labeled dataset • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: ImmunoCell-Bench
  • State Classification Accuracy target: 0.88
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Google Cloud · Optional
  • DeepSeek · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • pydantic_validation · Weight 1
  • state_classification_accuracy · Weight 1

Metrics

  • state_classification_accuracy · Target 0.88

Benchmarks

  • ImmunoCell-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing key gene marker levels (CD11b, CLEC7A, APOE, CX3CR1)

    Output: JSON containing microglial_state (string) and activation_index (float 0-1)

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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