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Deep Learning-Based Identification of Pathogenicity Genes in Phytophthora infestans Using Time-Series

Yinfei Dai1,2, Shihao Lu2, Jie Fan2

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

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Summary
This summary is machine-generated.

We developed a novel AI model to identify genes related to potato late blight pathogenicity. This approach aids in discovering new targets for breeding resistant potato cultivars and improving crop protection strategies.

Keywords:
LSTM–TransformerPhytophthora infestansdeep learningpotato late blighttime-series transcriptomic

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Area of Science:

  • Plant Pathology
  • Computational Biology
  • Genomics

Background:

  • Potato (Solanum tuberosum L.) is a vital global food crop, with China being a major producer.
  • Late blight significantly limits potato production, necessitating strategies for resistance.
  • Current methods for identifying pathogenicity genes lack temporal dynamic insights.

Purpose of the Study:

  • To introduce an LSTM-Transformer hybrid model for discovering pathogenicity-related genes from gene expression time-series data.
  • To identify novel candidate genes and pathways involved in potato late blight.
  • To provide a data-driven framework for enhancing potato resistance breeding.

Main Methods:

  • Utilized a time-series gene expression dataset of 32,917 genes across 18 samples (3 infection time points x 6 replicates).
  • Developed and applied a hybrid LSTM-Transformer deep learning model with a biologically informed temporal-attention architecture.
  • Implemented a gene time-series-specific data partitioning strategy and an interpretable deep analysis module.

Main Results:

  • Identified 200 high-confidence pathogenicity-related genes from potato late blight infection data.
  • Discovered enrichment of these genes in 15 key biological pathways, including plant immunity and stress responses.
  • Uncovered potential new roles for candidate genes in defense hormone pathways and cell wall modification.

Conclusions:

  • The study provides a powerful AI-driven framework for prioritizing genes from time-series expression data.
  • Identified molecular targets and functional groups crucial for potato late blight resistance.
  • Offers valuable insights for developing improved, blight-resistant potato cultivars through advanced breeding programs.