Application of mass spectrometry and integrative ─omics towards heterogeneous disease diagnosis

Complex heterogeneous diseases such as colorectal cancer and Parkinson’s disease display different clinical and pathological characteristics between patients. The integration of multiple biomarkers that correlate with disease stage and patient phenotype are urgently required to better classify disease subtypes based on molecular heterogeneity. High-resolution mass spectrometry coupled to machine learning-based integrative -omics are applied to identify differentially expressed molecular fingerprints associated with clinical phenotypes. This research identified a spectrum of potential lipids and proteins that are useful to classifying disease subtypes and as a holistic approach, could be used in the future to improve disease diagnosis.