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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Updated: Jun 18, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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Published on: July 25, 2013

A reinforcement learning-enhanced fuzzy multi-objective equilibrium optimization framework for multiple sequence

Hamidreza Hosseini1, Najme Mansouri2, Behnam Mohammad Hasani Zade1

  • 1Department of Computer Science, Shahid Bahonar University of Kerman, Box No. 76135-133, Kerman, Iran.

Scientific Reports
|June 16, 2026
PubMed
Summary

A new hybrid optimization framework, MOFSACEO-MSA, improves multiple sequence alignment (MSA) by balancing accuracy, conserved regions, and gap control. It shows superior performance on challenging RNA datasets, outperforming existing methods.

Keywords:
BioinformaticsEquilibrium optimizationFuzzy multi-objectiveMultiple sequence alignmentReinforcement learning

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Multiple sequence alignment (MSA) is crucial for genomics and evolutionary studies.
  • MSA is a complex multi-objective optimization problem, especially for large, diverse sequence sets.
  • Existing methods struggle with accuracy, conserved region preservation, and gap control.

Purpose of the Study:

  • To develop a novel hybrid optimization framework for high-quality multiple sequence alignment.
  • To address the challenges of accuracy, conserved regions, and gap proliferation in MSA.
  • To enhance adaptability and robustness across diverse biological sequence datasets.

Main Methods:

  • Proposed MOFSACEO-MSA: a hybrid framework integrating fuzzy multi-objective evaluation, Equilibrium Optimizer (EO), and Soft Actor-Critic (SAC).
  • Formulated MSA as a dynamic multi-objective problem using residue-level and column-level criteria (Sum-of-Pairs score, conservation, entropy, gap statistics).
  • Employed fuzzy logic for objective harmonization and SAC for adaptive parameter regulation of EO.

Main Results:

  • MOFSACEO-MSA achieved competitive or superior Sum-of-Pairs scores compared to 12 established tools.
  • Significantly reduced gap proportions and maintained compact alignment lengths.
  • Demonstrated improved robustness and convergence on large, heterogeneous RNA datasets.

Conclusions:

  • MOFSACEO-MSA offers a flexible and extensible optimization paradigm for high-quality MSA.
  • Effectively combines evolutionary search and reinforcement learning for challenging alignment tasks.
  • Shows particular strength in aligning large and diverse RNA sequence collections.