Predicting Human Essential Genes Using Deep Learning: MLP with Adaptive Data Balancing

Artificial intelligence (AI) has transformed many scientific disciplines including bioinformatics. Essential gene prediction is one important use of artificial intelligence in bioinformatics since it is necessary for knowledge of the biological pathways needed for cellular survival and disease diagnosis. Essential genes are fundamental for maintaining cellular life as well as for the survival and reproduction of organisms. Understanding the importance of these genes can help one to identify the basic needs of organisms, point out genes connected to diseases, and enable the development of new drugs. Traditional methods for identifying these genes are time consuming and costly, so computational approaches are used as alternatives. In this study, a Multi-Layer Perceptron (MLP) model combined with ADASYN (adaptive synthetic sampling). Furthermore, using deep learning techniques to solve the restrictions of traditional machine learning techniques and raise forecast accuracy attracts a lot of interest. It was proposed to handle data imbalance. The model utilizes features from protein-protein interaction networks, DNA and protein sequences. The model achieved high performance, with a sensitivity of 0.98, overall accuracy of 0.94, and specificity of 0.96, demonstrating its effectiveness in data classification.

@article{AbdElsalam2025,
  title     = {Predicting Human Essential Genes Using Deep Learning: MLP with Adaptive Data Balancing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ahmed AbdElsalam and Mohamed Abdallah and Hossam Refaat},
  doi       = {10.14569/IJACSA.2025.0160433},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160433}
}