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Abstract <p>Monogenetic disorders result from mutations in a single gene, making early diagnosis essential for preventive healthcare. Timely and accurate identification is crucial for effective clinical intervention, genetic counseling, and personalized treatment. However, the high dimensionality, redundancy, and complexity of genomic data present major challenges for traditional diagnostic models. In this study, a novel hybrid framework of Adaptive Elistic Genetic Algorithm Boosted Feedforward Neural Network for Monogenetic Disorder Diagnosis (AEGA-BoostFNN-MonoDx) Model is proposed for monogenetic disorder prediction. This approach integrates with an Adaptive Elistic Genetic Algorithm (AEGA) with a Boosted Feedforward Neural Network (BoostFNN) to enhance the predictive accuracy. The AEGA module is designed to perform intelligent feature selection using adaptive mutation and elitist strategies. This ensures the identification of highly informative and non-redundant features while minimizing the computational complexity. The optimized features are then used to train the BoostFNN classifier, which captures the complex nonlinear patterns in genomic sequence data. The model is evaluated by using datasets such as the Of Genomes And Genetics Datasets and Genetic Variant Classifications Datasets, encoded using biologically meaningful representations such as k-mer and one-hot encoding. The AEGA-BoostFNN-MonoDx Model outperforms the existing model, achieving 98.75% recall, 98.94% accuracy, 98.69% specificity, 98.77% f1-score, and 98.81% precision.Therefore, the proposed model contributes to advancing the results by providing a reliable and adaptable model for the early monogenetic disorders.</p>

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Reliable Genomic Data Classification for Monogenetic Disorders Using AEGA-BoostFNN-MonoDx Hybrid Deep Learning Framework

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Akila Rajini Selvaraj,
  • Mahalakshmi Poopathy,
  • Saranya Priyadharshini Ravi Shanker,
  • Ram Ganesh G.H.,
  • Nivetha Ganesan,
  • Raj Kannan Chandrasekaran,
  • Nandhini Krishna Kumar

摘要

Abstract

Monogenetic disorders result from mutations in a single gene, making early diagnosis essential for preventive healthcare. Timely and accurate identification is crucial for effective clinical intervention, genetic counseling, and personalized treatment. However, the high dimensionality, redundancy, and complexity of genomic data present major challenges for traditional diagnostic models. In this study, a novel hybrid framework of Adaptive Elistic Genetic Algorithm Boosted Feedforward Neural Network for Monogenetic Disorder Diagnosis (AEGA-BoostFNN-MonoDx) Model is proposed for monogenetic disorder prediction. This approach integrates with an Adaptive Elistic Genetic Algorithm (AEGA) with a Boosted Feedforward Neural Network (BoostFNN) to enhance the predictive accuracy. The AEGA module is designed to perform intelligent feature selection using adaptive mutation and elitist strategies. This ensures the identification of highly informative and non-redundant features while minimizing the computational complexity. The optimized features are then used to train the BoostFNN classifier, which captures the complex nonlinear patterns in genomic sequence data. The model is evaluated by using datasets such as the Of Genomes And Genetics Datasets and Genetic Variant Classifications Datasets, encoded using biologically meaningful representations such as k-mer and one-hot encoding. The AEGA-BoostFNN-MonoDx Model outperforms the existing model, achieving 98.75% recall, 98.94% accuracy, 98.69% specificity, 98.77% f1-score, and 98.81% precision.Therefore, the proposed model contributes to advancing the results by providing a reliable and adaptable model for the early monogenetic disorders.