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Challenge

Interactome Mutation Impact Analyzer using LlamaIndex and Hugging Face

Mapping autism-related mutations to protein-protein interaction networks requires retrieving high-dimensional structural and functional data. Construct a RAG-driven knowledge graph system using LlamaIndex and Hugging Face models to predict interactome disruption scores. Achieve a Pearson correlation of >= 0.85 against validated experimental perturbation scores.

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Challenge brief

What you are building

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

Build a structural interactome query engine using LlamaIndex and Hugging Face models to analyze disease mutation effects.

Delivery guide

How work is evaluated

Evaluation

Evaluates correlation between predicted interactome disruption scores and experimental binding assay data.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

score_range_check

Ensure disruption score is bounded 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

pearson_correlation

Pearson correlation coefficient between predicted and experimental disruption scores • target: 0.85 • range: -1-1

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

  • Build a domain-specific Knowledge Graph Index with LlamaIndex

  • Integrate Hugging Face embeddings and ESM protein language models

  • Calculate protein interaction perturbance scores for missense mutations

  • Query multi-modal structural proteomic datasets using structured Cypher/SPARQL semantics

Resources and assets

Reference links and supporting material

Dataset notes

Experimentally validated dataset of 300 autism-associated gene mutations with quantitative protein-protein interaction loss/gain measurements.

How this agent runs

Evaluates correlation between predicted interactome disruption scores and experimental binding assay data.

Challenge input

JSON with protein_a, protein_b, mutation (e.g. 'P102L'), and wildtype structures.

LlamaIndex

Framework required for combining structural knowledge graph indexing with LLM reasoning.

Hugging Face

Provides pre-trained ESM protein language model embeddings.

Evaluated output

JSON object with calculated_disruption_score (0.0 to 1.0) and supporting network node path.

Checks for
  • Ensure disruption score is bounded between 0.0 and 1.0.
  • Pearson correlation coefficient between predicted and experimental disruption scores • target: 0.85 • range...
Proof of success
  • Benchmark: Proteomic Mutation Perturbation Benchmark
  • Pearson Correlation target: 0.85
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Hugging Face · Required
  • Exa · Optional
  • Llama Index · Optional

Evaluation contract

  • score_range_check · Weight 1
  • pearson_correlation · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Interactome Mutation Impact Analyzer using LlamaIndex and Hugging Face