feat: hackathon/team-consortia

[ansh-info]

For authors

Description

1. Summary:

   • Implemented a specialized biomodel_tool that analyzes SBML models and matches species to knowledge graph entities
   • Used this tool to process user queries about biomodels
   • Configured the tool with paths to streamline the Streamlit user experience
   • Added starter questions to facilitate common biomodel analysis tasks

2. Context and Motivation:

   • The biomodel_tool extracts species from SBML models and uses RAG to generate descriptions from scientific literature
   • It then calculates embeddings for these descriptions and performs similarity matching with entities in the PrimeKG knowledge graph
   • This enables researchers to quickly identify connections between biomodel components and known biological entities
   • The paths simplify the user experience while maintaining the tool's analytical capabilities

3. Dependencies:

   • basico for SBML model parsing
   • langchain for RAG implementation
   • PyTorch for tensor operations and similarity calculations
   • PrimeKG for knowledge graph integration

The tool bridges the gap between systems biology models and established knowledge graphs, enabling researchers to gain deeper insights into model components and their biological significance with minimal technical overhead.

Contributor(added them as co-author is the commit message -> except the one mentioned -> I was not able to establish contact with them, through teams as well)

(Team 1 and Team 2)

• Ansh Kumar
• Rakesh
• Vishal Vervani
• Aikya Sira Kumar
• Jiakuan Zhao
• Rafiq hk
• David Outland ####

Streamlit App

The Streamlit application provides an intuitive chat interface where users can:

• Select from predefined starter questions about biomodels
• View detailed match results between model species and knowledge graph entities
• Explore descriptions of biological components generated from scientific literature

This interactive interface makes complex biomodel analysis accessible to researchers without requiring deep technical knowledge of the underlying tools and datasets.

Step 1: Load Model and Get Species

Step 2: Generate Species Descriptions

Step 3: Generate Embeddings and Map to Knowledge Graph

Step 4 Query PrimeKG and UniProt(Best approach)

Fixes # (issue) Mention the issue number.

Type of change

Please delete options that are not relevant.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested?

Please describe the tests you conducted to verify your changes. These may involve creating new test scripts or updating existing ones.

• Added new test(s) in the tests folder
• Added new function(s) to an existing test(s) (e.g.: tests/testX.py)
• No new tests added (Please explain the rationale in this case)

Checklist

• My code follows the style guidelines mentioned in the Code/DevOps guides
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation (e.g. MkDocs)
• My changes generate no new warnings
• I have added or updated tests (in the tests folder) that prove my fix is effective or that my feature works
• New and existing tests pass locally with my changes
• Any dependent changes have been merged and published in downstream modules

For reviewers

Checklist pre-approval

• Is there enough documentation?
• If a new feature has been added, or a bug fixed, has a test been added to confirm good behavior?
• Does the test(s) successfully test edge/corner cases?
• Does the PR pass the tests? (if the repository has continuous integration)

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• Does this PR close an issue? If so, please make sure to descriptively close this issue when the PR is merged.

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