Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays

BMC Bioinformatics

Table 2 Significant biomarkers after ICA analysis

	Significant Biomarkers	Physiological Processes
Asthma	Platelet Count	Thrombogenesis
	Serum Sodium	Serum sodium
	ALT	Tissue injury
	Neutrophil Percent	Acute inflammation
Type 1 Diabetes	Serum Sodium	Serum sodium
	Blood Urea Nitrogen	Kidney function
	Platelet Count	Thrombogenesis
	TSH3	Thyroid function
Type 2 Diabetes	Serum Sodium	Serum sodium
	ALT	Tissue injury
	Platelet Count	Thrombogenesis
Duchenne Muscular Dystrophy	Lactate Dehydrogenase	Tissue injury
	Triglycerides	Lipogenesis
	Platelet Count	Thrombogenesis
	AST	Tissue injury
Cystic Fibrosis	Total IgE	IgE antibody response
	Alkaline Phosphatase	Dephosphorylation
	Platelet Count	Thrombogenesis

Significant biomarkers for each disease are shown. Each significant biomarker was identified as a significant and statistically independent physiological factor from the patient laboratory data by ICA analysis (see Methods). Each significant biomarker was matched to a broader physiological process using a standard reference for clinical laboratory chemistry.

ISSN: 1471-2105