A Health App for Post-Pandemic Years (HAPPY) for people with physiological and psychosocial distress during the post-pandemic era: Protocol for a randomized controlled trial.

Objective: This article describes a protocol for a randomized controlled trial to evaluate the effects of a three-level Health App for Post-Pandemic Years (HAPPY) on alleviating post-pandemic physiological and psychosocial distress.

Methods: Convenience and snowball sampling methods will be used to recruit 814 people aged 18+ with physiological and/or psychosocial distress. The experimental group will receive a 24-week intervention consisting of an 8-week regular supervision phase and a 16-week self-help phase.

Genetic programming based ensemble system for microarray data classification.

Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average.

Molecular pattern discovery based on penalized matrix decomposition.

A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering.