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Challenge

Urine microRNA Pancreatic Cancer Screening Pipeline with Claude Agents SDK and OpenHands

Early detection of pancreatic cancer requires detecting subtle microRNA signatures in non-invasive urine samples. You will build an autonomous agent using Claude Agents SDK and OpenHands to execute quality control, feature selection, and classification pipelines targeting 90% diagnostic specificity.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an autonomous bioinformatics agent with Claude Agents SDK and OpenHands to execute urine microRNA biomarker discovery pipelines.

Delivery guide

How work is evaluated

Evaluation

Evaluates diagnostic classification accuracy and specificity on unseen urine miRNA validation cohorts.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

specificity_threshold_test

Ensure specificity is >= 0.90 at required sensitivity cutoff

Binary check

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

Dimension 2

roc_auc

Area under the receiver operating characteristic curve • target: 0.93 • 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

What you should walk away with

  • Construct autonomous agent workflows using Anthropic's Claude Agents SDK

  • Utilize OpenHands environment tools for execution of terminal actions and data inspection

  • Classify pancreatic cancer risk from urine-derived microRNA expression profiles

Resources and assets

Reference links and supporting material

Dataset notes

Urine microRNA screening matrix dataset covering 500 pancreatic cancer patients and healthy controls.

Challenge setup

Evaluates diagnostic classification accuracy and specificity on unseen urine miRNA validation cohorts.

Challenge input

CSV file path containing miRNA expression count matrix

Claude Agents SDK

Mandatory Anthropic agent orchestration SDK.

OpenHands

Provides execution environments and tooling.

Evaluated output

JSON array of patient IDs with predicted pancreatic cancer probability

Evaluation checks
  • Ensure specificity is >= 0.90 at required sensitivity cutoff
  • Area under the receiver operating characteristic curve • target: 0.93 • range: 0-1
Configured evaluation evidence
  • Benchmark: OncoDiagnostics-Bench
  • Roc Auc target: 0.93
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Environment
  • OpenHands · Required
Action Space
  • OpenHands · Required
  • PyTorch Lightning · Optional
  • Meta · Optional
Orchestration
  • OpenHands · Required

Evaluation contract

  • specificity_threshold_test · Weight 1
  • roc_auc · Weight 1

Metrics

  • roc_auc · Target 0.93

Benchmarks

  • OncoDiagnostics-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: CSV file path containing miRNA expression count matrix

    Output: JSON array of patient IDs with predicted pancreatic cancer probability

    Execution harness: Python

Evaluation tools

  • Claude Agents SDK · Agent Core Framework
  • OpenHands · Agent Workspace Environment

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Urine microRNA Pancreatic Cancer Screening Pipeline with Claude Agents SDK and OpenHands