SUMMARY: Because cancer has heterogeneous clinical behaviors due to the progressive accumulation of multiple genetic and epigenetic alterations, the identification of robust molecular signatures for predicting cancer outcome is profoundly important. Here, we introduce the APPEX Web-based analysis platform as a versatile tool for identifying prognostic molecular signatures that predict cancer diversity. We incorporated most of statistical methods for survival analysis and implemented seven survival analysis workflows, including CoxSingle, CoxMulti, IntransSingle, IntransMulti, SuperPC, TimeRoc and multivariate. A total of 236 publicly available datasets were collected, processed and stored to support easy independent validation of prognostic signatures. Two case studies including disease recurrence and bladder cancer progression were described using different combinations of the seven workflows. AVAILABILITY AND IMPLEMENTATION: APPEX is freely available at http://www.appex.kr. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Various approaches have been proposed for clinical protocol development, but few attentions have been paid to the ubiquitous healthcare services. This paper introduces a clinical protocol development methodology which is applicable to ubiquitous healthcare services. The methodology presents a systematic procedure which helps build up a protocol. It focuses on how to elucidate all participants' role and tasks, and how to efficiently specify the workflow of tasks. The methodology employs the so-called 6W1H model to specify constituent tasks of a clinical protocol. It provides a Petri net-based visualization method which generates graphical workflow for a clinical protocol described by the 6W1H model. The proposed methodology has been successfully applied to specify a ubiquitous Overactive Bladder Syndrome (OAB) patient care service protocol.