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ROptimus: a parallel general-purpose adaptive optimization engine.

Nicholas A G Johnson1, Liezel Tamon1, Xin Liu1

  • 1Radcliffe Department of Medicine, MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, United Kingdom.

Bioinformatics (Oxford, England)
|May 4, 2023
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Summary

ROptimus is a new R package for probabilistic optimization, enhancing computational biology by improving parameter sampling and avoiding local minima traps. This general-purpose engine offers flexible Monte Carlo optimization for diverse modeling tasks.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Probabilistic optimization is crucial for computational biology, but existing methods often struggle with inefficient parameter space exploration and getting stuck in local minima.
  • Developing robust optimization protocols is essential for accurately capturing system states in configurational space.

Purpose of the Study:

  • To develop a general-purpose optimization engine in R for seamless parameter sampling and rigorous optimization.
  • To provide a flexible tool that can be integrated into various modeling initiatives, from simple to complex.

Main Methods:

  • The R package ROptimus implements simulated annealing and replica exchange algorithms.
  • It utilizes adaptive thermoregulation with constrained acceptance frequency and unconstrained adaptive pseudo-temperature regimens for Monte Carlo optimization.
  • The engine offers lucid interfacing functions for easy integration into existing workflows.

Main Results:

  • ROptimus demonstrates applicability across a diverse range of problems, including data analyses and computational biology tasks.
  • The package facilitates efficient exploration of parameter spaces and mitigates the issue of trapping into local minima.
  • Rigorous parameter sampling is achieved through its advanced optimization protocols.

Conclusions:

  • ROptimus provides a powerful and flexible R-based solution for probabilistic optimization in computational biology and data analysis.
  • Its design addresses limitations of existing methods, offering improved performance and ease of use.
  • The freely available package enhances the capabilities of researchers in parameter estimation and system modeling.