Research On Risk And Survival Prediction Of Complex Diseases Based On Deep Learnin

With the development of genome sequencing technology and the application of deep learning techniques,the prediction of complex disease risk and survival has become a hot topic in current research.However,the low heritability of complex diseases,long sample genome sequence length,sparse effective information,and high noise pose significant challenges to risk prediction.In addition,survival prediction for complex diseases faces problems such as scarce and valuable samples,and difficulty in effectively utilizing multiple types of genomic data.To address these issues,this paper explores the research on deep learning-based prediction of complex disease risk and survival,with the aim of improving prediction accuracy and reliability.Aiming at the challenges faced in complex disease risk prediction research,such as long input sequence length,sparse effective information,and high noise,this paper proposes a novel single nucleotide polymorphism multi-channel encoding scheme and an Attention-based Polygenic Risk Score Model(APRSM).The APRSM model can utilize multi-channel encoding or One-Hot encoding inputs,and selectively focus on the most representative information features in the genomic sequence using selfattention mechanism to predict the risk of complex diseases.Experimental results on coronary artery disease and type 2 diabetes show that the APRSM model can effectively improve the accuracy and stability of prediction results compared to baseline methods and can guide further biological analysis based on the predicted results.Aiming at the challenges faced in complex disease survival prediction research,this paper proposes a Multi-Channel Breast Cancer Survival Prediction model(MCBSP)to effectively utilize multiple types of genomic data.The MCBSP model,composed of single-type module,a shared module,and a multi-channel fusion module,which can use gene expression data,gene mutation accumulation,single nucleotide variations,and gene copy number variations to extract effective information and fuse the four types of data for survival prediction.Experimental results show that the model can improve the accuracy of survival prediction for breast cancer.Overall,this paper explores deep learning-based risk and survival prediction for complex diseases.The proposed models for both tasks exhibit good prediction accuracy,providing a reference for personalized treatment and prevention of complex diseases.