Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.

ABSTRACT: Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein-protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors loc

SUBMITTER: Chuang YH 

PROVIDER: S-EPMC8526703 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature