Autism PPI Network Perturbation Analysis Agent with Google ADK
Understanding how autism mutations disrupt protein-protein interaction networks requires multi-omic graph analysis. Build a Google ADK agent hosted on Baseten to analyze PPI perturbation scores and report network centrality changes across thousands of neurodevelopmental gene variants.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a protein interaction network analysis agent using Google ADK deployed via Baseten to compute topological graph disruptions.
How work is evaluated
Evaluates graph perturbation scoring accuracy and output formatting.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Centrality Delta Range Check
Ensures calculated deltas fall within [-1.0, 1.0].
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Graph Metric Mean Absolute Error
MAE between computed centrality changes and expected network values. • target: 0.01 • range: 0-0.5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct multimodal graph agents using Google Agent Development Kit (ADK).
Deploy custom low-latency inference functions on Baseten platform.
Analyze network topology changes (betweenness centrality, degree centrality) induced by missense mutations.
Generate interactome perturbation summary matrices.
Reference links and supporting material
Protein-protein interaction graph dataset containing wildtype interactomes and mutant edge loss annotations for neurodevelopmental genes.
How this agent runs
Evaluates graph perturbation scoring accuracy and output formatting.
Challenge input
JSON with mutated gene, wildtype PPI edge list, and altered binding edges
Google ADK
Provides structure for building multimodal analytical agents.
Baseten
Delivers fast microservice deployment for graph computation.
Evaluated output
JSON with delta_degree_centrality, delta_betweenness, and hub_status
- Ensures calculated deltas fall within [-1.0, 1.0].
- MAE between computed centrality changes and expected network values. • target: 0.01 • range: 0-0.5
- Benchmark: ProteomicNetwork-Bench
- Graph Metric Mean Absolute Error target: 0.01
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Google · Required
- Google ADK · Optional
- Baseten · Optional
Evaluation contract
- Centrality Delta Range Check · Weight 1
- Graph Metric Mean Absolute Error · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.