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Visualization Methods for DNA Sequences: A Review and Prospects.

Tan Li1, Mengshan Li1, Yan Wu2

  • 1School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China.

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|November 27, 2024
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Summary
This summary is machine-generated.

This study reviews DNA sequence visualization methods, proposing knowledge graphs and machine learning for better bioinformatics analysis. These approaches enhance biological data interpretation and intelligent search systems.

Keywords:
computational biologygraphical representationknowledge graphmachine learningvisualization method

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomic Data Analysis
  • Evolutionary Biology

Background:

  • Efficient analysis of biological sequence data is a significant challenge in bioinformatics.
  • Graphical representation offers an intuitive method for DNA sequence analysis, but existing approaches are fragmented.
  • There is a need for organized, integrated, and advanced visualization techniques.

Purpose of the Study:

  • To review current 2D and 3D DNA sequence visualization methods.
  • To propose knowledge graphs and machine learning as key areas for advancing biological sequence visualization.
  • To provide a comprehensive framework for future research in this domain.

Main Methods:

  • Review of existing 2D and 3D DNA sequence visualization techniques.
  • Exploration of knowledge graph construction for biological sequence visualization.
  • Summary of machine learning techniques, including graph embedding and convolutional neural networks (CNNs).

Main Results:

  • Identified limitations in current DNA sequence visualization methods.
  • Proposed knowledge graphs and machine learning as promising avenues for enhanced visualization.
  • Detailed relevant theories, techniques, and models for knowledge graph construction and ML application.

Conclusions:

  • Knowledge graphs and machine learning offer a novel direction for biological sequence visualization.
  • These methods can provide valuable insights for computational biology, bioinformatics, and evolutionary analysis.
  • The study serves as a reference for improving intelligent search, knowledge bases, and query systems in bioinformatics.