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Predicting pathogenic single nucleotide variants (SNVs) in non-coding regions of the human genome presents a significant challenge for the extreme class imbalance between pathogenic “positive” variants and physiological “negative” ones, since most machine learning methods are biased toward predicting negative examples. We designed two “block-shaped” tabular-DNN architectures: a Modular Block-Deep Neural Network (MoB-DNN) and a tabular Residual Network (T-ResNet), able to address the class imbalance problem through a mini-batch balancing strategy. We employed a hierarchical optimization approach to efficiently tune hyper-parameters related to training procedure, architecture, batch size, and mini-batch balancing ratio. Our experimental results demonstrate that T-ResNet outperforms and MoB-DNN shows competitive performance with a state-of-the-art hyper-ensemble method, suggesting that residual connections provide significant advantages for capturing complex patterns in non coding regions of the human genome.

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Modular Deep Neural Networks with Residual Connections for Predicting the Pathogenicity of Genetic Variants in Non Coding Genomic Regions

  • 会议论文

  • 发表日期：

    2026年

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  • Federico Stacchietti,
  • Marco Nicolini,
  • Leonardo Chimirri,
  • Peter N. Robinson,
  • Elena Casiraghi,
  • Giorgio Valentini

摘要

Predicting pathogenic single nucleotide variants (SNVs) in non-coding regions of the human genome presents a significant challenge for the extreme class imbalance between pathogenic “positive” variants and physiological “negative” ones, since most machine learning methods are biased toward predicting negative examples. We designed two “block-shaped” tabular-DNN architectures: a Modular Block-Deep Neural Network (MoB-DNN) and a tabular Residual Network (T-ResNet), able to address the class imbalance problem through a mini-batch balancing strategy. We employed a hierarchical optimization approach to efficiently tune hyper-parameters related to training procedure, architecture, batch size, and mini-batch balancing ratio. Our experimental results demonstrate that T-ResNet outperforms and MoB-DNN shows competitive performance with a state-of-the-art hyper-ensemble method, suggesting that residual connections provide significant advantages for capturing complex patterns in non coding regions of the human genome.