Path-ATT-CNN: A Novel Deep Neural Network Method for Key Pathway Identification of Lung Cancer

Abstract

Attention convolutional neural networks (ATT-CNNs) have got a huge gain in picture operating and nature language processing. Shortage of interpretability cannot remain the adoption of deep neural networks. It is very conspicuous that is shown in the prediction model of disease aftermath. Biological data are commonly revealed in a nominal grid data structured pattern. ATT-CNN cannot be applied directly. In order to figure out these issues, a novel method which is called the Path-ATT-CNN is proposed by us, making an explicable ATT-CNN model based on united omics data by making use of a recently characterized pathway image. Path-ATT-CNN shows brilliant predictive demonstration difference in primary lung tumor symptom (PLTS) and non-primary lung tumor symptom (non-PLTS) when applied to lung adenocarcinomas (LADCs). The imaginational tool adoption which is linked with statistical analysis enables the status of essential pathways which finally exist in LADCs. In conclusion, Path-ATT-CNN shows that it can be effectively put into use elucidating omics data in an interpretable mode. When people start to figure out key biological correlates of disease, this mode makes promising power in predicting illness.

Keywords: ATT-CNN; Path-ATT-CNN; neural network; pathways; primary lung tumor symptom.