Cancer Human Disease Networks (cHDNs) via Deep Learning SEER-Medicare

Music City Center 

Cancer patients often also suffer from other disease conditions. For more effective management and treatment, it is crucial to understand the "big picture". Human disease network (HDN) analysis provides an effective way for describing the interrelationships among diseases. The goal of this study is to mine the SEER-Medicare data and construct the HDNs for major cancer types for the elderly. For network construction, we adopt penalized deep neural networks (pDNNs). The DNNs can be more flexible than the regression-based and other analyses, and penalization can effectively distinguish important disease interconnections from noises. As a "byproduct", we establish the asymptotic properties of pDNNs. The constructed cHDNs are carefully analyzed in terms of node, module, and network properties.

human disease network

deep learning

SEER-Medicare

cancer