Bio-Inspired Metaheuristic Optimization Algorithms for Biomarker Identification in Mass Spectrometry Analysis

Abstract

Mass spectrometry is an emerging technique that is continuously gaining momentum among bioinformatics researchers who intend to study biological or chemical properties of complex structures such as protein sequences. This advancement also embarks in the discovery of proteomic biomarkers through accessible body fluids such as serum, saliva, and urine. Recently, literature reveals that sophisticated computational techniques mimetic survival and natural processes adapted from biological life for reasoning voluminous mass spectrometry data yields promising results. Such advanced approaches can provide efficient ways to mine mass spectrometry data in order to extract parsimonious features that represent vital information, specifically in discovering disease-related protein patterns in complex proteins sequences. This article intends to provide a systematic survey on bio-inspired approaches for feature subset selection via mass spectrometry data for biomarker analysis.