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<p>This study investigates the use of deep learning models to predict disease status from single nucleotide polymorphism (SNP) data. Eight GEO datasets were processed using a consistent pipeline involving genotype encoding, data cleaning, and multiple feature selection strategies. A variety of DL architectures-including feedforward networks, autoencoders, CNNs, and RNNs-were trained and evaluated. The novelty of this work lies in the standardized preprocessing, feature selection, and model training pipeline applied across all datasets, allowing for a direct and fair comparison of model performance. Results consistently showed that feedforward networks and autoencoders performed best across most datasets. This work offers a practical approach to applying deep learning in genomics with potential for future extensions.</p>

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Harnessing deep learning for SNP-based disease prediction in genomics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月4日

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  • Colten Alme,
  • Harun Pirim,
  • M. Mishkatur Rahman

摘要

This study investigates the use of deep learning models to predict disease status from single nucleotide polymorphism (SNP) data. Eight GEO datasets were processed using a consistent pipeline involving genotype encoding, data cleaning, and multiple feature selection strategies. A variety of DL architectures-including feedforward networks, autoencoders, CNNs, and RNNs-were trained and evaluated. The novelty of this work lies in the standardized preprocessing, feature selection, and model training pipeline applied across all datasets, allowing for a direct and fair comparison of model performance. Results consistently showed that feedforward networks and autoencoders performed best across most datasets. This work offers a practical approach to applying deep learning in genomics with potential for future extensions.