Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization.

ABSTRACT:

Aim

To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets.

Materials and methods

GCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased.

Results

A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater

SUBMITTER: Baliban RC 

PROVIDER: S-EPMC3543478 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature