AI Tools for Exploring Systems Biology Resources



This report summarizes a study evaluating the capabilities of public Artificial Intelligence (AI) tools for exploring systems biology resources, particularly in mathematical modeling. The research focused on analyzing data in formats standardized by the COMBINE initiative.

Key Findings:

  • Benefits for Novices: Public AI offers significant advantages for beginners in systems biology by effectively identifying and presenting crucial information from complex models in an understandable format.
  • Key Information Extraction: AI tools like Meta AI can recognize key species in models, even from abbreviated names.
  • Mathematical Analysis: Platforms such as HyperWrite demonstrate the ability to accurately analyze mathematical expressions within models and explain the rationale behind specific mathematical choices.
  • Format Compatibility: Public AI tools exhibit better comprehension of commonly used data formats like SBML.

Limitations and Cautions:

  • Potential for Inaccuracy: AI-generated conclusions may be incorrect, with inconsistencies observed in biological sequence identification across different tools.
  • Lack of Reproducibility: Responses from public AI are not consistently reproducible, varying with slight changes in phrasing or repeated queries.
  • File Size and Truncation Issues: Free versions often have file size limitations, leading to inaccurate and inconsistent responses with larger, more complex models.
  • Superficial Details and Inconsistencies: While generally detailed, AI responses may include false or superficial details and exhibit inconsistencies upon repeated questioning.

Recommendations for Effective Use:

  • Cross-Platform Comparison: Comparing outputs from multiple AI platforms is crucial to mitigate errors and misinformation.
  • Critical Evaluation: AI-generated responses should be treated with caution, requiring critical review and cross-verification.

Conclusion:

Public AI tools show promise in facilitating the understanding of systems biology data and reducing the learning curve associated with diverse data formats, databases, and software. For novice users exploring systems biology, freely accessible AI tools can be valuable learning aids, provided users remain vigilant for inconsistencies and cross-verify responses. However, for advanced applications like de novo model development or novel analysis, public AI is not yet recommended due to potential inaccuracies and the need for user-defined assumptions. AI can play a supportive role in suggesting model improvements and explaining simulation results.

Source: Leveraging public AI tools to explore systems biology resources in mathematical modeling

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