International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Feature Selection with Ensemble Learning for Prostate Cancer Prediction from Gene Expression

  • Abass, Yusuf Aleshinloye (Department of Computer Science Nile University of Nigeria) ;
  • Adeshina, Steve A. (Department of Computer Science Nile University of Nigeria)
  • 투고 : 2021.12.05
  • 발행 : 2021.12.30
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초록

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

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과제정보

The authors thank the National Information Technology Development Agency (NITDA) and Nile University of Nigeria (NUN) for supporting Y.A. Abass's studies in Nigeria. The authors thank the anonymous reviewers for their objective remarks and their suggestions on the paper.

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