Mass Spectrometry Analysis via Metaheuristic Optimization Algorithms

Abstract

Biologically inspired metaheuristic techniques for extracting salient features from mass spectrometry data has been recently gaining momentum among related fields of research viz., bioinformatics and proteomics. Such sophisticated approaches provide efficient ways to mine voluminous mass spectrometry data in order to extract potential features by getting rid of redundant information. This feature extraction process ultimately aids in discovering disease-related protein patterns in complex mixtures that is easily obtained from biological fluids such as serum and urine. This article provides an overview of such typical bio-inspired approaches.