The invention discloses a
deep learning method for predicting the prognosis risk of a
cancer patient based on multi-
omics data, which is used for predicting the prognosis risk of the
cancer patient and comprises the following steps: S1, acquiring clinical data Y of a target
cancer patient and corresponding multi-
omics expression data X from an existing public
data set; S2, constructing a deep neural network; S3, updating the weight theta of the cancer multi-
omics data Xp and the
clinical information Yp of the patient of the existing common
data set through the constructed deep neural network to obtain a pre-training network Np based on the common
data set; S4, training the network Np again until the training frequency epoch reaches the operation upper limit, thereby obtaining a risk prediction network Nf; and S5, selecting the first n
gene features of the Importance coefficient of the target cancer patient by using an XGboost
algorithm, and improving the risk prediction network Nf to obtain a final risk prediction model. According to the method, the robustness of the prediction model is improved, and the prognosis risk of the cancer patient is predicted more accurately by using multi-
omics data.