AFS-DEA: An automatic feature selection platform for differential expression analysis

The majority of effective information on express profile data remains under-utilized due to the characteristics of high-dimensional features, complex correlations among sample attributes and the lack of skills and resources to analyze and visualize these data. In this paper, we developed an automatic feature selection platform combined with an intuitive and interactive interface for differential expression analysis (abbreviated as AFSDEA), which utilized ensemble learning to automatically select features on expression profile data. Qualitative and quantitative analyses indicating the interpretable and predictive ability of the selected features were provided. Correspondingly, experimental results on simulated and real data proved the effectiveness of the developed platform. The establishment of this platform generates a custom Web browser for automatically selecting features and provides outputs for differential expression analysis, which will facilitate the research on expression profile data. The webserver of AFS-DEA is available at http://bio-nefu.com/afs-dea.