Abstract
Analysis of complexity of biological time-series data is investigated to gain knowledge about the biosignal predictability. Using modern biological data such as mass spectral, this complexity information can be utilized to identify novel biomarkers for drug discovery, early disease detection and therapeutic treatment. To enhance the complexity analysis, a probabilistic fusion scheme, which is an alternative to the assumption of the independence of probabilistic models, is applied to synthesize the information given by different entropy methods.