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Multi-Omic Network Biology Collaboration System with CrewAI

Deciphering complex biological networks requires specialized collaborative agents for genomics, proteomics, and graph neural networks. You will construct a CrewAI multi-agent team that analyzes GPU-accelerated biological network graphs, producing validated therapeutic target leads with 88% cross-modality agreement.

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What you are building

Orchestrate a multi-agent network with CrewAI to integrate genomic and proteomic knowledge graphs for drug target discovery.

How work is evaluated

Evaluation

Evaluates multi-agent target validation consensus and gene interaction pathway analysis.

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

target_consensus_validity

Ensure identified target is a known upstream/downstream effector in pathway

Binary check

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

Dimension 2

cross_modality_agreement

Agreement percentage between genomic and proteomic agent findings • 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

  • Build role-playing multi-agent systems using CrewAI framework

  • Assign specialized tasks across Genomics Analyst, Proteomics Specialist, and Target Lead Crew members

  • Process graph-based biological interaction networks to prioritize drug candidates

Resources

Dataset notes

Multi-omic target interaction graph dataset covering 2,000 biological nodes and 15,000 directed edges.

Challenge setup

Evaluates multi-agent target validation consensus and gene interaction pathway analysis.

Challenge input

JSON object with query_gene and background_pathway

CrewAI

Mandatory CrewAI role-playing framework.

Evaluated output

JSON object with consensus_target string and confidence_score float

Evaluation checks
  • Ensure identified target is a known upstream/downstream effector in pathway
  • Agreement percentage between genomic and proteomic agent findings • target: 0.88 • range: 0-1
Configured evaluation evidence
  • Benchmark: TargetDiscovery-Bench
  • Cross Modality Agreement 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
  • CrewAI · Required
  • crewAI · Optional
  • Zed · Optional

Evaluation contract

  • target_consensus_validity · Weight 1
  • cross_modality_agreement · Weight 1

Metrics

  • cross_modality_agreement · Target 0.88

Benchmarks

  • TargetDiscovery-Bench
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with query_gene and background_pathway

    Output: JSON object with consensus_target string and confidence_score float

    Execution harness: Python

Evaluation tools

  • CrewAI · Multi-Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Multi-Omic Network Biology Collaboration System with CrewAI