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A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine

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Developing robust artificial intelligence (AI) and machine learning (ML) methods requires complex biological data. We introduce HIBACHI software for simulating this data, enabling better evaluation of AI and ML algorithms.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning

Background:

  • Developing and evaluating artificial intelligence (AI) and machine learning (ML) methods for regression and classification necessitates access to diverse datasets.
  • Open data initiatives, particularly in genomics, have facilitated access to real-world experimental and observational data.
  • Real-world data alone is insufficient for method evaluation due to the absence of a known ground truth.

Purpose of the Study:

  • To address the lack of methods and software for simulating complex biological and biomedical data.
  • To introduce a novel method and prototype software for generating realistic, complex biological data.
  • To develop new simulation modeling approaches for discriminating between different machine learning methods.

Main Methods:

  • Presentation of the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method.
  • Development of prototype software for simulating complex biological and biomedical data.
  • Introduction of new methods for creating simulation models tailored for machine learning method discrimination.

Main Results:

  • HIBACHI enables the simulation of complex biological and biomedical data with known ground truth.
  • The developed methods facilitate the generation of data that allows for direct evaluation of signal-to-noise ratios.
  • The simulation models are designed to specifically differentiate the performance of various machine learning algorithms.

Conclusions:

  • The HIBACHI method and software provide a crucial resource for developing and validating AI and ML in biological and biomedical research.
  • Simulated data with known ground truth is essential for rigorous evaluation of computational methods.
  • This work advances the capability to create tailored simulation data for benchmarking machine learning algorithms in complex systems.