Multi-omics Cancer Classification Using Stacked Denoising Autoencoder (SDAE) and Deep Belief Network (DBN)


  • Nur Ainul Afieqah Nuzula
  • Haslina Hashim


The multi-omics datasets from liver cancer are specifically being analysed and integrated which then will be classified according to cancerous and non-cancerous type. However, the dataset may have outlier, missing data and redundancy that may impact the accuracy of the result. Thus, pre-processing data is an important step that will remove the entire problem. Feature selection will help to select the full requirements data to that the overall research has great quality and give benefits to the community and future studies. Deep neural network algorithms like Stacked Denoising Autoencoder (SDAE) and Deep Belief Network (DBN) is reliable to perform classification for omics data. This research is mainly to find the best algorithm that will produce better result for this classification of multi-omics of liver cancer data