Patent ID: 7873479

Claim:
A method for classifying whether a sample from an individual is associated with inflammatory bowel disease (IBD), said method comprising: (a) analyzing a sample obtained from said individual to determine the presence or level of an anti-neutrophil cytoplasmic antibody (ANCA), anti- Saccharomyces cerevisiae immunoglobulin A (ASCA-IgA), anti- Saccharomyces cerevisiae immunoglobulin G (ASCA-IgG), an anti-outer membrane protein C (anti-OmpC) antibody, an anti-flagellin antibody, and a perinuclear anti-neutrophil cytoplasmic antibody (pANCA) in said sample; and (b) applying a combination of at least two learning statistical classifier systems in tandem to the presence or level of said ANCA, ASCA-IgA, ASCA-IgG, anti-OmpC antibody, anti-flagellin antibody, and pANCA determined in step (a) to classify said sample as an IBD sample or non-IBD sample with an overall accuracy of at least 70%, wherein said combination of at least two learning statistical classifier systems comprises a classification and regression tree (C&RT) or random forest and a neural network, and wherein said C&RT or random forest is first applied to the presence or level of said ANCA, ASCA-IgA, RSCA-IgG, anti-OmpC antibody, anti-flagellin antibody, and pANCA determined in step (a) to generate a prediction or probability value.