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Updated: Dec 8, 2025

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DeepHE: Accurately predicting human essential genes based on deep learning.

Xue Zhang1,2, Wangxin Xiao3, Weijia Xiao4

  • 1Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.

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Predicting human essential genes computationally aids drug discovery. DeepHE, a new deep learning method integrating sequence and protein-protein interaction data, accurately identifies essential genes, outperforming traditional machine learning models.

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Predicting essential genes computationally accelerates drug discovery and reduces experimental costs.
  • Existing methods for human essential gene prediction are limited and require performance improvement.
  • Many machine learning approaches struggle with imbalanced datasets common in essential gene prediction.

Purpose of the Study:

  • To develop a deep learning-based method (DeepHE) for accurate human essential gene prediction.
  • To integrate sequence-derived features and protein-protein interaction (PPI) network information.
  • To address the imbalanced learning challenge in essential gene prediction.

Main Methods:

  • Utilized a deep learning network embedding technique to extract features from PPI networks.
  • Derived 89 sequence features from DNA and protein sequences.
  • Trained a multilayer neural network integrating both feature types.
  • Applied a cost-sensitive technique to handle imbalanced learning.

Main Results:

  • DeepHE achieved high performance in predicting human gene essentiality, with AUC > 94%, AP > 90%, and accuracy > 90%.
  • DeepHE significantly outperformed traditional machine learning models (SVM, Naïve Bayes, Random Forest, Adaboost).
  • Demonstrated the effectiveness of integrating sequence and PPI network features with deep learning.

Conclusions:

  • DeepHE provides an accurate and effective deep learning framework for human essential gene prediction.
  • The method successfully addresses the imbalanced learning problem inherent in this task.
  • Accurate prediction of human essential genes is achievable through advanced machine learning and integrated biological data.