Janaina Gomes Perbone Nunes, José Joaquim Penedos Amendoeira, Diná de Almeida Lopes Monteiro da Cruz, Kathie Lasater, Sheila Coelho Ramalho Vasconcelos Morais, Emilia Campos de Carvalho
Clinical judgment and diagnostic reasoning of nursing students in clinical simulation
Revista brasileira de enfermagem
integration analysis cancer multiomics data pancancer classification potential clinical applications various aspects tumor diagnosis analyzing clinically significant features providing precision medicine applications embedding feature selection highdimensional multiomics data clinically necessary recently deep learning algorithms become promising cancer multiomic integration analysis methods due powerful capability capturing nonlinear relationships developing effective deep learning architectures cancer multiomics embedding feature selection remains challenge researchers view high dimensionality heterogeneity paper propose novel twophase deep learning model named avbaemodfr pancancer classification avbaemodfr achieves embedding multi2multi autoencoder based adversarial variational bayes method performs feature selection utilizing dualnetbased feature ranking method avbaemodfr utilizes avbae pretrain network parameters improves classification performance enhances feature ranking stability modfr firstly avbae learns highquality representation among multiple omics features unsupervised pancancer classification design efficient discriminator architecture distinguish latent distributions updating forward variational parameters secondly propose modfr simultaneously evaluate multiomics feature importance feature selection training designed multi2one selector network efficient evaluation approach based average gradient random mask subsets avoid bias caused input feature drift conduct experiments tcga pancancer dataset compare four stateoftheart methods phase results show superiority avbaemodfr sota methods