How to choose biomarkers in view of parameter estimation

Abstract

In numerous applications in biophysics, physiology and medicine, the system of interest is studied by monitoring quantities, called biomarkers, extracted from measurements. These biomarkers convey some information about relevant hidden quantities, which can be seen as parameters of an underlying model. In this paper we propose a strategy to automatically design biomarkers to estimate a given parameter. Such biomarkers are chosen as the solution of a sparse optimization problem given a user-supplied dictionary of candidate features. The method is in particular illustrated with two realistic applications, one in electrophysiology and the other in hemodynamics. In both cases, our algorithm provides composite biomarkers which improve the parameter estimation problem.

Biomarker Identification of Complex Diseases/Disorders: Methodological Parallels to Parameter Estimation

“…This method is related to the cosine similarity outlined in eq 8 but on a normalized data set. Additionally, the method proposed by Gerbeau et al 41 looks to identify biomarkers through the framework of a parameter subset selection technique where the parameters are linear combination of biomarkers identified from a user-inputted biomarker dictionary. This method highlights the similarities between biomarker identification and parameter selection and the affect that one field can have on the other.…”

The Importance of Biomarkers in Research

“…The World Health Organization (WHO) has uttered a most exhaustive description of biomarkers as "almost any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical, or biological. The measured response may be functional and physiological, biochemical at the cellular level, or a molecular interaction" [1].…”