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The abstract examines the utilization of Convolutional Neural Network (CNN) models in predicting the Feno type from genotype data. This study explores the efficacy of CNN architectures in extracting meaningful patterns and features from genotype information to accurately predict the corresponding Feno type. Various CNN configurations and parameters are investigated, including data preprocessing techniques, layer structures, and training methodologies. The research aims to assess the predictive capabilities of CNN models and their potential in genotype-phenotype prediction tasks, contributing to the advancement of precision medicine and genetic diagnostics.

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CNN Models in Prediction of Feno Type from Genotype

  • 会议论文

  • 发表日期：

    2025年

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  • Guilherme M. Henriques,
  • Maryam Abbasi,
  • Daniel Martins,
  • Joel P. Arrais

摘要

The abstract examines the utilization of Convolutional Neural Network (CNN) models in predicting the Feno type from genotype data. This study explores the efficacy of CNN architectures in extracting meaningful patterns and features from genotype information to accurately predict the corresponding Feno type. Various CNN configurations and parameters are investigated, including data preprocessing techniques, layer structures, and training methodologies. The research aims to assess the predictive capabilities of CNN models and their potential in genotype-phenotype prediction tasks, contributing to the advancement of precision medicine and genetic diagnostics.