AN ALGORITHM FOR DERIVING COMBINATORIAL BIOMARKERS BASED ON RIDGE REGRESSION

AN ALGORITHM FOR DERIVING COMBINATORIAL BIOMARKERS BASED ON RIDGE REGRESSION

Blog Article

Motivation: Combinatorial biomarkers are considered more specific and sensitive than single markers in medical diagnos-tics and prediction, yet even detection of such these combinatorial biomarkers requires deep computational analysis.The principles of analytic combinatorics, linear and kernel ridge regression, and machine learning were applied to derive new combinatorial biomarkers of muscle damage.Results: Lactate, phosphate, and middle-chain fatty acids were Building Set most often included into biochemical combinatorial mark-ers, while the following physiological parameters were found to be prevalent: muscle isometric strength, H-reflex length, and contraction tone.

Several strongly correlated combinatorial biomarkers Spoons of muscle damage with high prediction accuracy scores were identified.The approach - based on computational methods, regression algorithms and machine learning - provides a flexible, platform independent and highly extendable means of discovery and evaluation of combinatorial bi-omarkers alongside current diagnostic tools.Availability: The developed algorithm was implemented in Python programming language on a quantitative dataset com-prising 23 biochemical parameters, 37 physiological parameters and 3,903 observations.

The algorithm and our dataset are available free of charge on GitHub.Supplementary information: Supplementary data are available at Journal of Bioinformatics and Genomics online.