Large-scale quantitative shotgun tandem mass spectrometry serves as a flexible proteomic platform for the systematic investigation of molecular processes perturbed by disease and the potential discovery of clinically relevant protein biomarkers associated with a particular pathology. Multiple innovative profiling techniques have been introduced with the aims of comprehensively identifying and quantifying the protein complements of human tissues and blood and enabling the systematic comparison of clinically relevant specimens. In this review, the novel computational methods that have been developed to maximize the amount of information inferred from the raw spectral datasets are explored, including innovative database search programs for more complete and confident protein identifications, improved normalization and data processing techniques to enhance statistical accuracy, and innovative algorithms for improved pattern discrimination and biomarker candidate ranking. Ultimately, integrative biomarker analyses that amalgamate the protein expression data generated by these approaches, together with data from high-throughput functional genomics, offer the possibility of identifying the mechanisms and causative factors that underlie complex human diseases, thereby improving both clinical outcomes and personalized treatment options.