Multi-omics Data Integration Model based on Isomap and Convolutional Neural Network

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The recent advances in genetics technologies have led to high-throughput characterized different biological molecules’ functionalities. The availability of heterogeneous omics sparked the challenge of integrating them for further analysis. This work incorporates the Isomap technique to embed multi-omic data into a convolutional neural network (CNN). The deep learning model fuses three omics data, which are gene expression, copy number alteration (CNA), and DNA methylation data, for breast cancer stage prediction. Isomap is utilized to convert the high-dimensional data into 2-dimensional maps. The gene similarity network (GSN) map is created based on gene expression data to preserve gene relationships. The values from three omics for each sample are used to color the GSN map based on the RGB system. The created GSN maps for all samples are fed into the CNN for classification.The model was applied to TCGA breast invasive carcinoma data set to predict the stage of breast cancer. It outperformed the state-of-art iSOM-GSN model in performance metrics, including accuracy, precision, recall, f1-measure, and area under the curve (AUC). The results indicate that a combination of Isomap embedding technique and CNN can successfully integrate a multi-omics data set for cancer outcome prediction, including the diagnosis and prognosis of the complex disease.