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Interspecific comparison of gene expression profiles using machine learning.

Artem S Kasianov1, Anna V Klepikova1, Alexey V Mayorov1

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|January 10, 2023
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Summary

This study introduces Interspecific Similarity of Expression Evaluated using Machine Learning (ISEEML), a new machine learning method for comparing gene function across species. ISEEML uses gene expression profiles to identify functional similarities, improving knowledge transfer from model to economically important plants.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Interspecific gene comparisons are crucial for biological research and translating knowledge from model organisms to economically important species.
  • Current methods relying on sequence analysis have low resolution and struggle with complex eukaryotic gene evolutionary histories, particularly in plants with extensive genome duplications.
  • There is a need for novel methods to compare gene function across species, especially for plants.

Purpose of the Study:

  • To develop and validate a novel machine learning-based algorithm, ISEEML, for interspecific gene classification based on functional similarity.
  • To introduce new metrics for expression pattern similarity (expression score - ES) suitable for species with differing morphologies.
  • To demonstrate the efficacy of ISEEML by comparing gene expression profiles of Arabidopsis thaliana, Zea mays, and Fagopyrum esculentum.

Main Methods:

  • Development of the Interspecific Similarity of Expression Evaluated using Machine Learning (ISEEML) algorithm.
  • Utilizing machine learning to compare gene expression profiles for inferring functional similarity.
  • Introduction of novel expression score (ES) metrics for quantifying expression pattern similarity across species.
  • Comparative analysis of transcriptome maps from Arabidopsis thaliana, Zea mays, and Fagopyrum esculentum.

Main Results:

  • The ISEEML classifier achieved an Area Under the Curve (AUC) of 0.91.
  • At an ES threshold of 0.5, the algorithm demonstrated a specificity of 94% and a sensitivity of 72%.
  • The study successfully demonstrated functional gene similarity inference across phylogenetically distant plant species.

Conclusions:

  • ISEEML offers a powerful, machine learning-driven approach for interspecific gene functional comparison, overcoming limitations of sequence-based methods.
  • The developed expression score (ES) metrics provide a robust way to assess functional similarity in genes across species with diverse morphologies.
  • This method facilitates more effective knowledge transfer from model plants to crops, advancing plant biology and agricultural applications.