A Comparative Study of CNN for Prediction of Human Cancer Types Integrating Protein-Protein Interaction Networks and Omics Data

This paper investigates convolutional neural networks (CNN) for predicting cancer types by integrating protein-protein interaction (PPI) networks with omics data. While [Chuang et al. 2021] employed a single 3-layer CNN, we explore ten different architectures, including a custom model developed by our team (CNN2Layers), following their methodology. By evaluating the strengths and weaknesses of these models, we aim to identify the most effective CNN for accurately predicting various human cancers. Our proposed model achieved state-of-the-art performance using fewer layers. Interestingly, the simpler architectures achieved superior results, indicating their effectiveness in handling the specific characteristics of the dataset.