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Cracking the genetic code with neural networks.

Marc Joiret1, Marine Leclercq2, Gaspard Lambrechts3

  • 1Biomechanics Research Unit, GIGA in Silico Medicine, Liège University, Liège, Belgium.

Frontiers in Artificial Intelligence
|April 24, 2023
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) can re-discover the genetic code. Neural networks require millions of data pairs and training epochs to accurately map codons to amino acids, including rare ones.

Keywords:
Artificial Intelligencecodon embeddingcodon usagedata efficiencydeep neural networkgenetic code decipheringnatural language processing

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Artificial Intelligence in Genomics

Background:

  • The genetic code, mapping codons to amino acids, is fundamental to molecular biology.
  • Traditional deciphering methods did not involve Artificial Intelligence (AI).

Purpose of the Study:

  • To investigate if neural networks can autonomously re-discover the genetic code.
  • To quantify the data and training requirements for AI-driven genetic code deciphering.

Main Methods:

  • Comparison of various Deep Learning neural network architectures.
  • Quantitative estimation of human transcriptomic training data size.
  • Assessment of codon embedding layers for semantic similarity.
  • Investigation of unbalanced amino acid representations.

Main Results:

  • Deep neural networks can efficiently learn genetic code deciphering.
  • Achieving 100% accuracy requires 4-22 million codon-amino acid pairs over 7-40 epochs.
  • Codon embeddings and unbalanced amino acid data accelerate rare codon deciphering.

Conclusions:

  • AI, specifically deep neural networks, can successfully re-discover the genetic code.
  • Significant training data and epochs are necessary for high accuracy, especially for rare codons.
  • Network architecture and data handling influence learning efficiency.