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Interpretable machine learning methods for predictions in systems biology from omics data.

David Sidak1, Jana Schwarzerová1,2, Wolfram Weckwerth1,3

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Summary

Machine learning models in systems biology can predict cellular behavior but often lack interpretability. This work introduces interpretable machine learning concepts for systems biologists to understand biological mechanisms from omics data.

Keywords:
deep learningexplainable artificial intelligenceinterpretable machine learningmetabolomicsmulti-omicsproteomicstranscriptomics

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Area of Science:

  • * Systems biology
  • * Computational biology
  • * Machine learning

Background:

  • * Machine learning (ML) is widely used in systems biology for predictions from complex 'omics' data.
  • * Current ML applications often prioritize prediction over understanding underlying biological mechanisms.
  • * There is a growing need for interpretable ML models to gain mechanistic insights and support critical decisions in biological research.

Purpose of the Study:

  • * To familiarize systems biologists with the concept and application of interpretable machine learning (IML).
  • * To address the ambiguity surrounding interpretability and provide a framework for IML in systems biology.
  • * To review and categorize existing studies using predictive ML on non-sequential omics data.

Main Methods:

  • * Discussion of data sets, preparation, ML methods, and software relevant to omics research.
  • * Introduction of perspectives from the IML community.
  • * Development of a scheme for categorizing omics data studies based on interpretability.

Main Results:

  • * A review and categorization of recent studies employing predictive ML models on non-sequential omics data.
  • * Identification of different approaches and challenges in achieving model interpretability.
  • * Proposed framework to guide future research in interpretable omics data analysis.

Conclusions:

  • * Interpretability is crucial for advancing biological understanding beyond mere prediction.
  • * A standardized approach to IML in systems biology is needed.
  • * This work provides a foundation for applying IML to extract deeper biological knowledge from omics data.