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Scalable machine learning-assisted model exploration and inference using Sciope.

Prashant Singh1, Fredrik Wrede1, Andreas Hellander1

  • 1Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden.

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The Sciope toolbox accelerates computational studies of gene regulatory networks using machine learning. It enables efficient, scalable inference and parameter exploration for complex models on various platforms.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Discrete stochastic models are crucial for studying gene regulatory networks.
  • Computational cost limits the analysis of complex, high-dimensional models.
  • Machine learning offers a promising approach to overcome these limitations.

Purpose of the Study:

  • To develop a scalable toolbox for machine learning-assisted inference and model exploration.
  • To facilitate the use and development of new algorithms in this domain.
  • To enable efficient analysis of complex stochastic gene regulatory networks.

Main Methods:

  • Development of the Sciope Python3 toolbox.
  • Implementation of machine learning-assisted methods for inference and parameter exploration.
  • Design for leveraging distributed and heterogeneous computational resources for parallelism.

Main Results:

  • Sciope supports advanced algorithms for likelihood-free inference and model exploration.
  • The toolbox is built for scalability and efficient use of computational resources.
  • It allows for convenient parallelism across diverse platforms, including cloud environments.

Conclusions:

  • Sciope provides a powerful, user-friendly platform for advancing computational systems biology.
  • It addresses the computational challenges in analyzing complex gene regulatory networks.
  • The toolbox promotes wider adoption and development of machine learning in biological modeling.