Extracting the Most Relevant Information from Bio-Photonic Data: A Constrained Maximum Entropy Methodology

This paper introduces a Constrained Maximum Entropy (CME) approach to extract knowledge from bio-photonic data. More specifically, we discuss the main issues related to this new type of data and show the potential of the CME methodology to introduce both a priori knowledge and data constraints to efficiently analyze bio-photon data. The advantage in our view is that it is possible to find the most unbiased bio photons distribution, that is the distribution which has the maximum entropy among the class of distributions satisfying constraints imposed by given knowledge and being least uncommitted to information not yet available. Then, we go a step further and suggest that the quantitative and qualitative constraints of the CME formulation based on accurate bio-photon emissions will provide a powerful new tool to monitor changes in biological systems and will be used to identify potential unstable states and evaluate the effect of a new treatment on a biological system.