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Challenge

Build a Vercel AI SDK & Baseten Protein Interaction Network Predictor

Autism spectrum disorder mutations disrupt complex protein interaction networks (PPIs). Build a full-stack TypeScript backend using the Vercel AI SDK that communicates with high-throughput graph neural network models hosted on Baseten to compute perturbation scores across network nodes.

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Challenge brief

What you are building

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

Develop a TypeScript application with Vercel AI SDK calling GPU models on Baseten to quantify autism mutation PPI perturbations.

Delivery guide

How work is evaluated

Evaluation

Evaluates TypeScript backend calculations of PPI node perturbation metrics.

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

network_score_bounds

Validates that network delta score falls between 0.0 and 1.0.

Binary check

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

Dimension 2

interactor_overlap_jaccard

Jaccard similarity between predicted affected network nodes and experimental PPI gold standard. • target: 0.85 • 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

  • Implement server-side streaming and tool calling with Vercel AI SDK in TypeScript

  • Integrate deep learning biological inference endpoints hosted on Baseten infrastructure

  • Compute node centrality and binding delta scores across autism PPI network graphs

  • Return structured JSON payloads for web-based molecular interaction visualizations

Resources and assets

Reference links and supporting material

Dataset notes

Network dataset of 20 high-confidence autism risk genes and their experimentally mapped physical protein interactors.

Challenge setup

Evaluates TypeScript backend calculations of PPI node perturbation metrics.

Challenge input

JSON containing gene_symbol (string) and variant_change (string).

AI SDK

Vercel framework for streaming and tool integration in JS/TS.

Baseten

Scalable cloud platform for high-throughput PyTorch model deployment.

Evaluated output

JSON containing affected_interactors (list of strings) and network_delta_score (float).

Evaluation checks
  • Validates that network delta score falls between 0.0 and 1.0.
  • Jaccard similarity between predicted affected network nodes and experimental PPI gold standard. • target: 0...
Configured evaluation evidence
  • Benchmark: Proteomics Disruption Index
  • Interactor Overlap Jaccard target: 0.85
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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

Action Space
  • Baseten · Required
  • Vercel AI SDK · Optional
  • Hostinger Website Builder · Optional
Orchestration
  • Vercel AI SDK · Optional

Evaluation contract

  • network_score_bounds · Weight 1
  • interactor_overlap_jaccard · Weight 1

Metrics

  • interactor_overlap_jaccard · Target 0.85

Benchmarks

  • Proteomics Disruption Index
  • biotech

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing gene_symbol (string) and variant_change (string).

    Output: JSON containing affected_interactors (list of strings) and network_delta_score (float).

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · TypeScript AI Framework
  • Baseten · Model Hosting

Recipe freeze marker

No recipe freeze time is recorded.

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