Author:
Tuan D. Pham
Affiliation:
School of Engineering and Information Technology, University of New South Wales, Australia
Keyword(s):
Entropy, Complexity, Geostatistics, Information Fusion, Mass Spectrometry Data.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Bioinformatics
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
;
Sensor Networks
;
Soft Computing
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.